notebook.ipynb

{
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   "source": [
    "# Graph Neural Nets for Credit Scoring\n",
    "\n",
    "**Note**: This is a trimmed down version of the SageMaker JumpStart Solution titled \"Graph-Based Credit Scoring\" for academic/education purposes. Please use the production version for purposes other than review, as this version does not use all SageMaker resources as the solution publicly released in JumpStart. For a brief overview, please see the related [blog](https://aws.amazon.com/blogs/machine-learning/build-a-corporate-credit-ratings-classifier-using-graph-machine-learning-in-amazon-sagemaker-jumpstart/). \n",
    "\n",
    "Credit scoring using just tabular data may be improved by exploiting data on business linkages, for which you may construct a graph, denoted as `CorpNet` -- for corporate network. You can then apply graph machine learning classification using GNNs on this graph and a tabular feature set for the nodes, to build a better ML model by further exploiting the information in network relationships. \n",
    "\n",
    "Graph Neural Nets (GNNs) are emerging as an interesting way to enhance existing machine learning models in several domains, see: https://www.amazon.science/blog/amazon-at-wsdm-the-future-of-graph-neural-networks\n",
    "\n",
    "#### If you run this notebook locally, please run with the default `Python3` kernel.\n",
    "\n",
    "#### If you run this notebook in Sagemaker Studio, please choose the below settings. \n",
    "- Kernel: `PyTorch 1.10 Python 3.8 GPU/CPU Optimized` \n",
    "- Use GPU machine: `ml.g4dn.xlarge` (4 core CPU, 1 GPU, 16 GB RAM), or `ml.t3.2xlarge` (8 core CPU, 32 GB RAM)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1e062b",
   "metadata": {},
   "source": [
    "## Abstract \n",
    "\n",
    "Credit ratings are traditionally generated using models that use financial statement data and market data, which is typically in tabular form, i.e., numeric and categorical data. This model constructs a network of firms using text from SEC filings and shows that using the network of firm relationships with tabular data can generate more accurate rating predictions. The model demonstrates a methodology to use big data to extend tabular data credit scoring models, which have been used by the ratings industry for decades, to the class of machine learning models on networks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8908d665",
   "metadata": {},
   "source": [
    "## What does this notebook do? \n",
    "\n",
    "This notebook (and related auxilliary program and data files) demonstrate the following: \n",
    "\n",
    "- *Network data*: The solution uses a network of connected companies using the MD&A section from SEC 10-K/Q filings. We assume that companies with similar forward looking statements are likely to be connected for credit events, though this is but one approach to constructing a corporate graph. These connections are represented in a graph. The solution provides a single function that generates the network. \n",
    "\n",
    "- *Tabular data*: For graph node features, the solution uses the variables in the Altman Z-score model and the industry category of each firm. These are provided in a synthetic dataset made available for demonstration purposes.\n",
    "\n",
    "- The graph data and tabular data are used to fit a rating classifier using graph neural nets (GNNs). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb64bc1",
   "metadata": {},
   "source": [
    "## Part 1: Constructing a Corporate Network using SEC Filings (`CorpNet`)\n",
    "\n",
    "- A simple approach is used to construct a network of credit connected companies using the text in the Management Discussion & Analysis (MD&A) section of the 10-K/Q filings, which are annual/quarterly reports companies are required to file with the SEC. The MD&A section usually contains the forward looking outlook for a company and also discusses trends and market conditions that may impact a firm. \n",
    "\n",
    "- To obtain the filings for each company that populates the `CorpNet`, use the AWS SageMaker JumpStart card titled \"Financial TabText Data Construction\". A blog showing how to do this is also available: https://aws.amazon.com/blogs/machine-learning/create-a-dashboard-with-sec-text-for-financial-nlp-in-amazon-sagemaker-jumpstart/. This data has already been collected and provided in the file `text_data.csv`.\n",
    "\n",
    "- The graph is based on the distribution of cosine similarities between document embeddings of the MD\\&A section of firms' 10-K/Q filings. The similarity of two firms is given by the following equation: $s = \\frac{A \\cdot B}{\\|A\\| \\cdot \\|B\\|}$, where $A, B$ are document embeddings. Since these embedding vectors may contain negative values, it is possible to return negative values. However, links in the network are based on values where $s>0.5$. The distribution of pairwise cosine similarities is shown below and the cutoff used is deep in the right tail. However, this is without loss of generality, and other cutoffs may be used to modulate the number of links in the network. \n",
    "\n",
    "![cosine_sims_synthdata](https://sagemaker-solutions-prod-us-east-2.s3.us-east-2.amazonaws.com/sagemaker-graph-neural-net-models-for-financial-classification/1.1.0/docs/cosine_sims_synthdata.png)\n",
    "\n",
    "\n",
    "- The degree distribution, i.e., the number of connections of each node in the graph, turns out to be power-law distributed, which is classic in small-world [scale-free](https://en.wikipedia.org/wiki/Scale-free_network) graphs, hence `CorpNet` appears to fall into this category of graphs. \n",
    "\n",
    "![degree_dist](https://sagemaker-solutions-prod-us-east-2.s3.us-east-2.amazonaws.com/sagemaker-graph-neural-net-models-for-financial-classification/1.1.0/docs/degree_dist.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca79ea0a",
   "metadata": {},
   "source": [
    "## Part 2: The synthetic dataset of credit scoring features\n",
    "\n",
    "The model used is a well-known bankruptcy prediction approach, from the seminal paper by Altman (1968). A brief summary is provided here. See also the description at this [site](https://www.creditguru.com/index.php/bankruptcy-and-insolvency/altman-z-score-insolvency-predictor). Similar information is also available on many other sites.\n",
    "\n",
    "The model uses 8 inputs converted into 5 financial ratios: (i) Current assets ($CA$); (ii) Current liabilities ($CL$); (iii) Total Liabilities ($TL$); (iv) earnings before interest and taxes ($EBIT$); (v) Total Assets ($TA$); (vi) Net Sales ($NS$); (vii) Retained earnings ($RE$); and (viii) Market value of equity ($MVE$). The relationships between these 8 items are modeled as stipulated in the Altman model through the following five ratios: \n",
    "- A: EBIT / Total Assets \n",
    "- B: Net Sales / Total Assets\n",
    "- C: Mkt Value of equity / Total Liabilities\n",
    "- D: Working Capital / Total Assets\n",
    "- E: Retained Earnings / Total Assets\n",
    " \n",
    "The Z-score is then provided by the following formula, estimated using Linear Discriminant Analysis (LDA) and the coefficients in the formula are published widely by Altman.\n",
    "\n",
    "$$\n",
    "Zscore = 3.3 A + 0.99 B + 0.6 C + 1.2 D + 1.4 E\n",
    "$$\n",
    "\n",
    "High quality companies have high Z-scores and low quality companies have low ones. The simulated Z-scores used to construct our synthetic dataset are calibrated to broad balance-sheet, income-statement, and market data using averages for the U.S. economy from the following sources: \n",
    "\n",
    "- [Balance sheet data](https://fred.stlouisfed.org/release/tables?rid=434&eid=196197)\n",
    "- [Income statement data](https://fred.stlouisfed.org/release/tables?rid=434&eid=195208)\n",
    "- [Price to book](http://pages.stern.nyu.edu/~adamodar/New\\_Home\\_Page/datafile/pbvdata.html)\n",
    "\n",
    "The numbers taken from these sources are used to extract total values for US companies for the 8 inputs mentioned above. In addition, we obtain the average price-to-book ($P2B$) ratio for US companies, book equity value ($EQ$) and then generate market value of equity ($MVE$) as $P2B(EQ+RE)$. Working capital ($WC$) is as usual, $CA-CL$, the difference between current assets and current liabilities. We normalized all simulated financial data by Total Assets. This normalization does not impact the Altman ratios in any way. \n",
    "\n",
    "This dataset of simulated companies and their financials is then enhanced with a column of ratings, where the ratings are mapped -- with some noise -- to the Z-score, such that higher Z-score firms are assigned higher quality ratings and lower Z-score firms get lower quality ratings. The dataset titled `tabular_data.csv` is also provided with this notebook. \n",
    "\n",
    "Given that the `CorpNet` graph is generated from real SEC filings, we compute \"quality\" scores for each MD&A section using JumpStart APIs for NLP scoring, see the JumpStart card titled \"NLP Score Dashboard for SEC Text\" and the related documentation [link](https://sagemaker-jumpstart-industry-pack.readthedocs.io/en/latest/smjsindustry.nlp_scorer.html). The quality scores are a net sum of positive NLP scores (positivity, polarity, safe, sentiment, certainty) and negative NLP scores (fraud, litigious, negative, uncertainty, risk). These quality scores are used to associate higher \"quality\" nodes with higher ratings firms in the synthetic tabular dataset. This injects a connection between `CorpNet` and the synthetic data.\n",
    "\n",
    "We used the (MD&A) section of companies' SEC 10-K/Q filings to construct a corporate graph, `CorpNet`, already discussed above. The approach is based on the idea that companies facing similar risks will evidence similarities in their forward-looking statements in the MD&A section. Therefore, we construct a graph where each node is a company in the dataset and two companies are linked if the cosine similarity of their MD&A document embeddings is greater than 0.5. The notebook that accompanies this solution contains this generated graph data in addition to the tabular data described above. \n",
    "\n",
    "Therefore, we generate both, a graph using real SEC filings and associate it with the synthetic tabular data generated to match broad financial averages for US firms. \n",
    "\n",
    "**References**\n",
    "\n",
    "- Altman, Edward I. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance 23, no. 4 (1968): 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.\n",
    "\n",
    "- Altman, Edward I. “A Fifty-Year Retrospective on Credit Risk Models, the Altman Z -Score Family of Models and Their Applications to Financial Markets and Managerial Strategies.” Journal of Credit Risk 14, no. 4 (December 11, 2018): 1–34.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41aa5d4",
   "metadata": {},
   "source": [
    "## Data Summary\n",
    "\n",
    "1. The dataset has tabular data such as various accounting ratios (numerical) and industry codes (categorical). The dataset has $N = 3286$ rows. Rating labels are also added. These are the node features to be used with graph machine learning. \n",
    "\n",
    "2. The dataset also contains a corporate graph, which is undirected and unweighted. This solution also allows the user to tweak the structure of the graph by varying the way in which links are included. \n",
    "\n",
    "3. Classification using GNNs can be multi-category for all ratings or binary, divided between investment grade (rating levels AAA, AA, A, BBB) and non-investment grade (rating levels BB, B, CCC, CC, C, D). D=defaulted.  \n",
    "\n",
    "Next we move on to using the graph and tabular data to demonstrate how GNNs are applied to this problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbcc11e",
   "metadata": {},
   "source": [
    ">**<span style=\"color:RED\">Important</span>**: \n",
    ">This solution is for demonstrative purposes only. It is not financial advice and should not be relied on as financial or investment advice. The associated notebooks, including the trained model, use synthetic data, and are not intended for production use. While text from SEC filings is used, the financial data is synthetically and randomly generated and have no relation to any company's true financials. Hence, the synthetically generated ratings also do not have any relation to a company's true rating. You are free to adapt this solution for your own use cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692f1e95",
   "metadata": {},
   "source": [
    "This solution relies on a config file to run the provisioned AWS resources. Run the cell below to generate that file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089fc797",
   "metadata": {},
   "source": [
    "## Part 3: Load up the required packages and data\n",
    "\n",
    "The solution uses several open source packages below and you should ensure that you comply with the terms of use of these packages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5e96e04-99e4-47ee-8306-6cba2fce4ccc",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Successfully built fairscale antlr4-python3-runtime seqeval future\n",
      "Installing collected packages: typish, tokenizers, text-unidecode, tensorboard-plugin-wit, sortedcontainers, sentencepiece, pyasn1, py4j, msgpack, heapdict, distlib, cymem, charset-normalizer, antlr4-python3-runtime, zict, xxhash, wrapt, werkzeug, wasabi, urllib3, tqdm, toolz, threadpoolctl, tensorboard-data-server, tenacity, tblib, tabulate, spacy-loggers, spacy-legacy, smart-open, rsa, regex, pyyaml, pyparsing, pygments, pyDeprecate, pydantic, pyasn1-modules, protobuf, portalocker, ordered-set, oauthlib, numpy, networkx, murmurhash, multidict, mdurl, locket, llvmlite, langcodes, kiwisolver, joblib, jmespath, importlib-metadata, grpcio, graphviz, future, fsspec, frozenlist, fonttools, filelock, fastprogress, fastcore, dill, Cython, cycler, colorama, cloudpickle, click, catalogue, cachetools, async-timeout, absl-py, yarl, yacs, virtualenv, typer, torchmetrics, tifffile, tensorboardX, srsly, scipy, PyWavelets, pyarrow, preshed, plotly, patsy, partd, pandas, opencv-python-headless, opencv-python, omegaconf, numba, nptyping, nltk, multiprocess, markdown-it-py, markdown, jsonschema, imageio, google-auth, fastdownload, fairscale, deprecated, contourpy, botocore, blis, autocfg, aiosignal, accelerate, xgboost, statsmodels, scikit-learn, scikit-image, s3transfer, rich, responses, requests-oauthlib, ray, pathy, nlpaug, model-index, matplotlib, hyperopt, huggingface-hub, gluonts, dask, confection, aiohttp, transformers, timm, thinc, sktime, seqeval, qudida, pytorch-metric-learning, pmdarima, openmim, lightgbm, google-auth-oauthlib, gluoncv, distributed, catboost, boto3, tensorboard, tbats, spacy, datasets, autogluon.common, albumentations, pytorch-lightning, fastai, evaluate, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.vision, autogluon.timeseries, autogluon.text, autogluon\n",
      "  Attempting uninstall: urllib3\n",
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      "Successfully installed Cython-0.29.33 PyWavelets-1.4.1 absl-py-1.4.0 accelerate-0.13.2 aiohttp-3.8.4 aiosignal-1.3.1 albumentations-1.1.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 autocfg-0.0.8 autogluon-0.6.2 autogluon.common-0.6.2 autogluon.core-0.6.2 autogluon.features-0.6.2 autogluon.multimodal-0.6.2 autogluon.tabular-0.6.2 autogluon.text-0.6.2 autogluon.timeseries-0.6.2 autogluon.vision-0.6.2 blis-0.7.9 boto3-1.26.70 botocore-1.29.70 cachetools-5.3.0 catalogue-2.0.8 catboost-1.1.1 charset-normalizer-3.0.1 click-8.0.4 cloudpickle-2.2.1 colorama-0.4.6 confection-0.0.4 contourpy-1.0.7 cycler-0.11.0 cymem-2.0.7 dask-2021.11.2 datasets-2.9.0 deprecated-1.2.13 dill-0.3.6 distlib-0.3.6 distributed-2021.11.2 evaluate-0.3.0 fairscale-0.4.6 fastai-2.7.10 fastcore-1.5.28 fastdownload-0.0.7 fastprogress-1.0.3 filelock-3.9.0 fonttools-4.38.0 frozenlist-1.3.3 fsspec-2023.1.0 future-0.18.3 gluoncv-0.10.5.post0 gluonts-0.11.9 google-auth-2.16.0 google-auth-oauthlib-0.4.6 graphviz-0.20.1 grpcio-1.43.0 heapdict-1.0.1 huggingface-hub-0.12.0 hyperopt-0.2.7 imageio-2.25.1 importlib-metadata-6.0.0 jmespath-1.0.1 joblib-1.2.0 jsonschema-4.8.0 kiwisolver-1.4.4 langcodes-3.3.0 lightgbm-3.3.5 llvmlite-0.39.1 locket-1.0.0 markdown-3.4.1 markdown-it-py-2.1.0 matplotlib-3.7.0 mdurl-0.1.2 model-index-0.1.11 msgpack-1.0.4 multidict-6.0.4 multiprocess-0.70.14 murmurhash-1.0.9 networkx-2.8.8 nlpaug-1.1.10 nltk-3.8.1 nptyping-1.4.4 numba-0.56.4 numpy-1.22.4 oauthlib-3.2.2 omegaconf-2.1.2 opencv-python-4.7.0.68 opencv-python-headless-4.7.0.68 openmim-0.2.1 ordered-set-4.1.0 pandas-1.5.3 partd-1.3.0 pathy-0.10.1 patsy-0.5.3 plotly-5.13.0 pmdarima-1.8.5 portalocker-2.7.0 preshed-3.0.8 protobuf-3.20.3 py4j-0.10.9.7 pyDeprecate-0.3.2 pyarrow-11.0.0 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydantic-1.10.4 pygments-2.14.0 pyparsing-3.0.9 pytorch-lightning-1.7.7 pytorch-metric-learning-1.3.2 pyyaml-6.0 qudida-0.0.4 ray-2.0.1 regex-2022.10.31 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.3.1 rsa-4.9 s3transfer-0.6.0 scikit-image-0.19.3 scikit-learn-1.1.3 scipy-1.8.1 sentencepiece-0.1.97 seqeval-1.2.2 sktime-0.13.4 smart-open-5.2.1 sortedcontainers-2.4.0 spacy-3.5.0 spacy-legacy-3.0.12 spacy-loggers-1.0.4 srsly-2.4.5 statsmodels-0.13.5 tabulate-0.9.0 tbats-1.1.2 tblib-1.7.0 tenacity-8.2.1 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorboardX-2.6 text-unidecode-1.3 thinc-8.1.7 threadpoolctl-3.1.0 tifffile-2023.2.3 timm-0.6.12 tokenizers-0.13.2 toolz-0.12.0 torchmetrics-0.8.2 tqdm-4.64.1 transformers-4.23.1 typer-0.7.0 typish-1.9.3 urllib3-1.25.11 virtualenv-20.19.0 wasabi-1.1.1 werkzeug-2.2.2 wrapt-1.14.1 xgboost-1.7.3 xxhash-3.2.0 yacs-0.1.8 yarl-1.8.2 zict-2.2.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# ! pip install autogluon.tabular==0.5.2\n",
    "\n",
    "!pip install -U pip\n",
    "!pip install -U setuptools wheel\n",
    "\n",
    "# CPU version of pytorch has smaller footprint - see installation instructions in\n",
    "# pytorch documentation - https://pytorch.org/get-started/locally/\n",
    "!pip install torch==1.12.1+cpu torchvision==0.13.1+cpu torchtext==0.13.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html\n",
    "\n",
    "!pip install autogluon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "265ae916",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25h  Created wheel for fst-pso: filename=fst_pso-1.8.1-py3-none-any.whl size=20430 sha256=716e035744179c30a6352d0cff907cb52c3930d5a6ed0d5e124ca348ba462bd5\n",
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      "\u001b[?25h  Created wheel for miniful: filename=miniful-0.0.6-py3-none-any.whl size=3513 sha256=53f63d844927fd7e2019c14a1ce51e1e8d8cedbf66bd1f67b4ef75bdd16c70c1\n",
      "  Stored in directory: /root/.cache/pip/wheels/ba/d9/a0/ddd93af16d5855dd9bad417623e70948fdac119d1d34fb17c8\n",
      "Successfully built sagemaker protobuf3-to-dict fst-pso miniful\n",
      "Installing collected packages: pytz, charset-normalizer, zipp, xyzservices, urllib3, tornado, threadpoolctl, smdebug_rulesconfig, smart-open, six, PyYAML, pyparsing, protobuf, ppft, pox, pillow, packaging, numpy, networkx, MarkupSafe, kiwisolver, joblib, jmespath, idna, fonttools, dill, cycler, contextlib2, certifi, attrs, scipy, schema, requests, python-dateutil, protobuf3-to-dict, multiprocess, Jinja2, importlib-resources, importlib-metadata, google-pasta, contourpy, simpful, scikit-learn, pathos, pandas, miniful, matplotlib, botocore, s3transfer, fst-pso, bokeh, pyfume, boto3, sagemaker, FuzzyTM, gensim\n",
      "\u001b[31mERROR: 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.\n",
      "sktime 0.13.4 requires numpy<1.23,>=1.21.0, but you have numpy 1.24.2 which is incompatible.\n",
      "sktime 0.13.4 requires scikit-learn<1.2.0,>=0.24.0, but you have scikit-learn 1.2.1 which is incompatible.\n",
      "sktime 0.13.4 requires scipy<1.9.0, but you have scipy 1.10.0 which is incompatible.\n",
      "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-vision 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-timeseries 0.6.2 requires networkx<3.0,>=2.3, but you have networkx 3.0 which is incompatible.\n",
      "autogluon-timeseries 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-timeseries 0.6.2 requires scipy<1.10.0,>=1.5.4, but you have scipy 1.10.0 which is incompatible.\n",
      "autogluon-tabular 0.6.2 requires networkx<3.0,>=2.3, but you have networkx 3.0 which is incompatible.\n",
      "autogluon-tabular 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-tabular 0.6.2 requires scikit-learn<1.2,>=1.0.0, but you have scikit-learn 1.2.1 which is incompatible.\n",
      "autogluon-tabular 0.6.2 requires scipy<1.10.0,>=1.5.4, but you have scipy 1.10.0 which is incompatible.\n",
      "autogluon-multimodal 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-multimodal 0.6.2 requires scikit-learn<1.2,>=1.0.0, but you have scikit-learn 1.2.1 which is incompatible.\n",
      "autogluon-multimodal 0.6.2 requires scipy<1.10.0,>=1.5.4, but you have scipy 1.10.0 which is incompatible.\n",
      "autogluon-multimodal 0.6.2 requires smart-open<5.3.0,>=5.2.1, but you have smart-open 6.3.0 which is incompatible.\n",
      "autogluon-features 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-features 0.6.2 requires scikit-learn<1.2,>=1.0.0, but you have scikit-learn 1.2.1 which is incompatible.\n",
      "autogluon-core 0.6.2 requires networkx<3.0,>=2.3, but you have networkx 3.0 which is incompatible.\n",
      "autogluon-core 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\n",
      "autogluon-core 0.6.2 requires scikit-learn<1.2,>=1.0.0, but you have scikit-learn 1.2.1 which is incompatible.\n",
      "autogluon-core 0.6.2 requires scipy<1.10.0,>=1.5.4, but you have scipy 1.10.0 which is incompatible.\n",
      "autogluon-common 0.6.2 requires numpy<1.24,>=1.21, but you have numpy 1.24.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed FuzzyTM-2.0.5 Jinja2-3.1.2 MarkupSafe-2.1.2 PyYAML-6.0 attrs-22.2.0 bokeh-3.0.3 boto3-1.26.70 botocore-1.29.70 certifi-2022.12.7 charset-normalizer-3.0.1 contextlib2-21.6.0 contourpy-1.0.7 cycler-0.11.0 dill-0.3.6 fonttools-4.38.0 fst-pso-1.8.1 gensim-4.3.0 google-pasta-0.2.0 idna-3.4 importlib-metadata-6.0.0 importlib-resources-5.10.2 jmespath-1.0.1 joblib-1.2.0 kiwisolver-1.4.4 matplotlib-3.7.0 miniful-0.0.6 multiprocess-0.70.14 networkx-3.0 numpy-1.24.2 packaging-23.0 pandas-1.5.3 pathos-0.3.0 pillow-9.4.0 pox-0.3.2 ppft-1.7.6.6 protobuf-3.20.3 protobuf3-to-dict-0.1.5 pyfume-0.2.25 pyparsing-3.0.9 python-dateutil-2.8.2 pytz-2022.7.1 requests-2.28.2 s3transfer-0.6.0 sagemaker-2.132.0 schema-0.7.5 scikit-learn-1.2.1 scipy-1.10.0 simpful-2.9.0 six-1.16.0 smart-open-6.3.0 smdebug_rulesconfig-1.0.1 threadpoolctl-3.1.0 tornado-6.2 urllib3-1.26.14 xyzservices-2022.9.0 zipp-3.13.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# %%capture \n",
    "!pip install -I gensim bokeh networkx sagemaker scikit-learn matplotlib # torch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "801da496",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from utils import construct_network_data\n",
    "import warnings\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06259a6c",
   "metadata": {},
   "source": [
    "## Tabular Feature Set\n",
    "\n",
    "We read in a file that contains all the data. It has the 5 Altman financial ratios A, B, C, D, E described in Part 2 above, and the binary rating class, as well as one-hot encoding for the industry categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec32f86c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "target_column = 'binary_rating'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3177f59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry_code</th>\n",
       "      <th>CurrentLiabs</th>\n",
       "      <th>TotalLiabs</th>\n",
       "      <th>RetainedEarnings</th>\n",
       "      <th>CurrentAssets</th>\n",
       "      <th>NetSales</th>\n",
       "      <th>EBIT</th>\n",
       "      <th>MktValueEquity</th>\n",
       "      <th>Rating</th>\n",
       "      <th>binary_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>D</td>\n",
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       "      <td>24.560932</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>20.135293</td>\n",
       "      <td>50.225308</td>\n",
       "      <td>29.748943</td>\n",
       "      <td>26.070400</td>\n",
       "      <td>15.765115</td>\n",
       "      <td>1.203600</td>\n",
       "      <td>228.732074</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D</td>\n",
       "      <td>17.417811</td>\n",
       "      <td>49.893589</td>\n",
       "      <td>29.480277</td>\n",
       "      <td>24.160795</td>\n",
       "      <td>12.612130</td>\n",
       "      <td>1.189818</td>\n",
       "      <td>236.955918</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  industry_code  CurrentLiabs  TotalLiabs  RetainedEarnings  CurrentAssets  \\\n",
       "0             D     20.868258   50.501544         32.547303      24.871286   \n",
       "1             D     19.622967   50.352084         28.760326      29.103598   \n",
       "2             D     21.474133   55.007104         29.903571      24.560932   \n",
       "3             I     20.135293   50.225308         29.748943      26.070400   \n",
       "4             D     17.417811   49.893589         29.480277      24.160795   \n",
       "\n",
       "    NetSales      EBIT  MktValueEquity  Rating  binary_rating  \n",
       "0  14.774910  1.334931      238.571513       2              1  \n",
       "1  12.447340  1.137064      215.396468       2              1  \n",
       "2  13.074464  1.390076      232.182228       2              1  \n",
       "3  15.765115  1.203600      228.732074       2              1  \n",
       "4  12.612130  1.189818      236.955918       2              1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "rating_df = pd.read_csv('datasets/tabular_data.csv')\n",
    "\n",
    "rating_df[target_column] = rating_df['Rating'].apply(lambda x: 1 if x in ['AAA', 'AA', 'A', 'BBB'] else 0)\n",
    "rating_df['Rating'] = le.fit_transform(rating_df['Rating'])\n",
    "\n",
    "rating_df.reset_index(inplace=True, drop=True)\n",
    "rating_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad667486",
   "metadata": {},
   "source": [
    "Finally, we prune the tabular feature set, by removing the label column `Rating`, which is for multiclass rating. This dataset still includes the label column `binary_rating` for binary classification, which is the use case we develop here. If multicategory classification is required, then we drop `binary_rating` instead and keep the `Rating` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60d25d8d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rating_df.drop(['Rating'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a035895f-fb9f-44b8-966c-ef94c0e8014a",
   "metadata": {},
   "source": [
    "We now examine how much of the dataset has label 1 verus label 0. We see that the dataset is quite balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d62902e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5410833840535606"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(rating_df[target_column].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02dab03",
   "metadata": {},
   "source": [
    "## Implementing Graph Construction\n",
    "\n",
    "In this section we use our custom approach to construct `CorpNet` using the text from SEC filings.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec82dfd3",
   "metadata": {},
   "source": [
    "Read the text data to construct network data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d30908",
   "metadata": {},
   "source": [
    "#### * Due to the github file size limit, we split our dataset into 9 files, each of which has size < 25 MB. In the below cell, we read all files and concatenate into a single DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58263d04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "dfs = [pd.read_csv(f, header=0) for f in glob.glob('datasets/dataset_*.csv')]\n",
    "text_df = pd.concat(dfs,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f32173",
   "metadata": {},
   "source": [
    "### Construct the network\n",
    "1. For each source node, i.e., a row in the dataset, find its neighbor nodes sorted from nearest to furthest based on some distance measurement. The distance measurement in this example is computed based on the embeddings of a text column **MDNA** generated from the **Doc2Vec** method. However, you may use embeddings from any algorithm, such as BERT, or financial embeddings from a pre-trained [RoBERTa-SEC model](https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/). \n",
    "\n",
    "2. For each source node, add a link if the document vector for a node has high cosine similarity with another node. Note that it is possible to generate zero links for some nodes. However, these nodes are still included in the graph. \n",
    "\n",
    "3. Add links, i.e., destination nodes for all nodes that are close in embedding space. \n",
    "\n",
    "4. The length of the `src` and `dst` lists will be the number of links in the network. They should both have the same length. \n",
    "\n",
    "5. The number of source nodes with links will differ from the number of destination nodes with links. Some nodes will be isolated, i.e., not linked at all. Both these numbers will be less than $N$. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b39ccb",
   "metadata": {},
   "source": [
    "#### Drop isolated nodes and renumber all nodes\n",
    "\n",
    "We will use the Deep Graph Library ([DGL](https://www.dgl.ai/)) for graph machine learning. DGL requires that all nodes are consecutively numbered, and the code below ensures no missing node numbers in a sequence of nodes, so all nodes are numbered consecutively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a32d07cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes = 3286\n",
      "Min of cosine similarities = -0.11409335252945868\n",
      "Mean of cosine similarities = 0.2891669157968984\n",
      "Median of cosine similarities = 0.2805087755673509\n",
      "Number of nodes = 3286\n",
      "Number of links (symmetric) = 170613.0\n",
      "Average degree =  103.84236153377967\n",
      "Check: number of links = 341226\n",
      "CPU times: user 3.31 s, sys: 1.37 s, total: 4.69 s\n",
      "Wall time: 2.75 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#src_dst_dict = construct_network_data(text_df, text_column_name=\"MDNA\", embedding_size=300, cutoff=0.5)\n",
    "\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "window_size=5\n",
    "min_count=1\n",
    "cutoff=0.5\n",
    "\n",
    "embedding_matrix = pd.read_csv(\"embed_matrix.csv\", header=None)\n",
    "similars = cosine_similarity(embedding_matrix.values)\n",
    "np.fill_diagonal(similars, 0.0)\n",
    "n_nodes = embedding_matrix.shape[0]\n",
    "x = similars.reshape(n_nodes*n_nodes,1)\n",
    "n_links = len(x[x>cutoff])/2\n",
    "\n",
    "# Examine the distribution of cosine similarities and other statistics of the graph\n",
    "#plt.hist(similars.reshape(n_nodes*n_nodes,1),100); plt.grid()\n",
    "print(\"Number of nodes =\", n_nodes)\n",
    "print(\"Min of cosine similarities =\", np.min(similars))\n",
    "print(\"Mean of cosine similarities =\", np.mean(similars))\n",
    "print(\"Median of cosine similarities =\", np.median(similars))  \n",
    "print(\"Number of nodes =\", n_nodes)\n",
    "print(\"Number of links (symmetric) =\",n_links)\n",
    "print(\"Average degree = \", 2*n_links/n_nodes)\n",
    "\n",
    "# Construct the source and destination node lists\n",
    "# Note that this delivers an undirected, symmetric graph - modify for directed graph\n",
    "src = []\n",
    "dst = []\n",
    "for i in range(n_nodes):\n",
    "    for j in range(n_nodes):\n",
    "        if similars[i,j]>cutoff:\n",
    "            src.append(i)\n",
    "            dst.append(j)\n",
    "print(\"Check: number of links =\", len(src))\n",
    "\n",
    "src_dst_dict = {'src': src, 'dst': dst}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8376a027",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del_nodes = set(rating_df.index) - (set(src_dst_dict['src'] + src_dst_dict['dst']))\n",
    "\n",
    "rating_df.drop(list(del_nodes), inplace=True)\n",
    "rating_df['node'] = rating_df.index\n",
    "rating_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "270178ff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "node_map = {v:k for k,v in rating_df['node'].to_dict().items()}\n",
    "\n",
    "src_dst_dict['src'] = [node_map[n] for n in src_dst_dict['src']]\n",
    "src_dst_dict['dst'] = [node_map[n] for n in src_dst_dict['dst']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ae95425",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest index = 3284\n",
      "# nodes = 3285\n",
      "# source nodes with links = 3285\n",
      "# destination nodes with links = 3285\n",
      "# Linked nodes = 3285\n",
      "# isolated nodes = 0\n"
     ]
    }
   ],
   "source": [
    "# Check\n",
    "print('Highest index =', max(rating_df.index))\n",
    "print('# nodes =', len(rating_df))\n",
    "print('# source nodes with links =', len(set(src_dst_dict['src'])))\n",
    "print('# destination nodes with links =',len(set(src_dst_dict['dst'])))\n",
    "print('# Linked nodes =', len(set(src_dst_dict['src'] + src_dst_dict['dst'])))\n",
    "print('# isolated nodes =', len(rating_df) - len(set(src_dst_dict['src'] + src_dst_dict['dst'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f704c30",
   "metadata": {},
   "source": [
    "This completes the preparation of the corporate graph, denoted as `CorpNet`. We save the source and destination nodes to store `CorpNet`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3475016",
   "metadata": {},
   "source": [
    "## Part 4: Train a baseline model without graph machine learning\n",
    "\n",
    "It is useful to set a baseline that mimics a model that only uses the tabular data for each firm. This will enable a comparison of models that gain from enhancing the features used with graph data. In order to be agnostic to the baseline model and to set a competitive baseline, AutoGluon Tabular using a SageMaker AutoGluon Container is used to fit ML models the tabular data. AutoGluon has been shown to beat most other AutoML packages in kaggle competitions, discussed in this [blog](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/). It uses stack-ensembling over a wide range of models and exploits the tabular feature set comprehensively. In contrast, for graph ML we use the [deep graph library (DGL)](https://www.dgl.ai/) that only fits one GNN which may be optimally chosen with hyperparmater optimization and will likely only beat AutoGluon in accuracy if there is sufficient information in the graph that adds value to the classifier. \n",
    "\n",
    "The next few blocks of code will train a rating classifier on the tabular features in this solution. \n",
    "\n",
    "Begin by uploading the train and test data to a S3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "208eccc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_df, test_df = train_test_split(rating_df, test_size=0.2, random_state=46, stratify=rating_df[target_column].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bb12562",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2628, 10), (657, 10))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d616d5e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir -p ag_processed_data\n",
    "ag_train_df = train_df.drop([\"node\"], axis=1)\n",
    "ag_test_df = test_df.drop([\"node\"], axis=1)\n",
    "ag_train_df.to_csv(\"ag_processed_data/train_data.csv\", header=True, index=False)\n",
    "ag_test_df.to_csv(\"ag_processed_data/test_data.csv\", header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7a4ad1b-9524-4383-af51-d3c8ad42d4a0",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # ! pip install autogluon.tabular==0.5.2\n",
    "\n",
    "# !pip3 install -U pip\n",
    "# !pip3 install -U setuptools wheel\n",
    "\n",
    "# # CPU version of pytorch has smaller footprint - see installation instructions in\n",
    "# # pytorch documentation - https://pytorch.org/get-started/locally/\n",
    "# !pip3 install torch==1.12.1+cpu torchvision==0.13.1+cpu torchtext==0.13.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html\n",
    "\n",
    "# !pip3 install autogluon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4394758c-02da-4df3-9abe-ab79592c27cf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from autogluon.tabular import TabularDataset, TabularPredictor\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224b3675",
   "metadata": {},
   "source": [
    "We run model training in an AutoGluon-Tabular. Recall that the classification task is a binary one, to predict whether a company is investment grade or below investment grade.\n",
    "\n",
    "The next block of code shows how simple the code is to run AutoGluon-Tabular in local mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36c5c594",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded data from: ag_processed_data/train_data.csv | Columns = 9 / 9 | Rows = 2628 -> 2628\n",
      "Loaded data from: ag_processed_data/test_data.csv | Columns = 9 / 9 | Rows = 657 -> 657\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ag-results\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"ag-results/\"\n",
      "AutoGluon Version:  0.6.2\n",
      "Python Version:     3.8.15\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #100~18.04.2-Ubuntu SMP Tue Nov 29 08:39:52 UTC 2022\n",
      "Train Data Rows:    2628\n",
      "Train Data Columns: 8\n",
      "Label Column: binary_rating\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  [0, 1]\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    130613.04 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.3 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('float', [])  : 7 | ['CurrentLiabs', 'TotalLiabs', 'RetainedEarnings', 'CurrentAssets', 'NetSales', ...]\n",
      "\t\t('object', []) : 1 | ['industry_code']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', []) : 1 | ['industry_code']\n",
      "\t\t('float', [])    : 7 | ['CurrentLiabs', 'TotalLiabs', 'RetainedEarnings', 'CurrentAssets', 'NetSales', ...]\n",
      "\t0.0s = Fit runtime\n",
      "\t8 features in original data used to generate 8 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.15 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.05s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'f1'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.1902587519025875, Train Rows: 2128, Val Rows: 500\n",
      "Fitting 13 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.6945\t = Validation score   (f1)\n",
      "\t0.42s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.6934\t = Validation score   (f1)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.7528\t = Validation score   (f1)\n",
      "\t1.13s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.7509\t = Validation score   (f1)\n",
      "\t0.75s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.7435\t = Validation score   (f1)\n",
      "\t0.6s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.7413\t = Validation score   (f1)\n",
      "\t0.6s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.7517\t = Validation score   (f1)\n",
      "\t1.14s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.757\t = Validation score   (f1)\n",
      "\t0.57s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.7561\t = Validation score   (f1)\n",
      "\t0.56s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "No improvement since epoch 1: early stopping\n",
      "\t0.7444\t = Validation score   (f1)\n",
      "\t1.63s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\t0.7461\t = Validation score   (f1)\n",
      "\t0.71s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch ...\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "\t0.7673\t = Validation score   (f1)\n",
      "\t1.81s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.7401\t = Validation score   (f1)\n",
      "\t1.24s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t0.7807\t = Validation score   (f1)\n",
      "\t2.0s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 13.89s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"ag-results/\")\n",
      "INFO:root:Best model: WeightedEnsemble_L2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_test  score_val  pred_time_test  pred_time_val   fit_time  pred_time_test_marginal  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0            LightGBMXT    0.733252   0.752756        0.004516       0.005229   1.125643                 0.004516                0.005229           1.125643            1       True          3\n",
      "1       NeuralNetFastAI    0.725389   0.744425        0.022272       0.016977   1.625138                 0.022272                0.016977           1.625138            1       True         10\n",
      "2   WeightedEnsemble_L2    0.719251   0.780656        0.438755       0.297751  12.127677                 0.006257                0.002294           1.999840            2       True         14\n",
      "3        NeuralNetTorch    0.718499   0.767318        0.010082       0.008931   1.808069                 0.010082                0.008931           1.808069            1       True         12\n",
      "4              LightGBM    0.718157   0.750877        0.003175       0.005329   0.746166                 0.003175                0.005329           0.746166            1       True          4\n",
      "5         LightGBMLarge    0.714849   0.740132        0.003829       0.005545   1.238546                 0.003829                0.005545           1.238546            1       True         13\n",
      "6        ExtraTreesGini    0.713514   0.757042        0.118266       0.080784   0.570871                 0.118266                0.080784           0.570871            1       True          8\n",
      "7      RandomForestGini    0.710027   0.743546        0.107432       0.080802   0.601843                 0.107432                0.080802           0.601843            1       True          5\n",
      "8              CatBoost    0.709766   0.751748        0.003382       0.004394   1.140350                 0.003382                0.004394           1.140350            1       True          7\n",
      "9      RandomForestEntr    0.709239   0.741259        0.108831       0.081084   0.599754                 0.108831                0.081084           0.599754            1       True          6\n",
      "10       ExtraTreesEntr    0.709239   0.756098        0.127520       0.081601   0.561924                 0.127520                0.081601           0.561924            1       True          9\n",
      "11              XGBoost    0.700272   0.746060        0.032023       0.005865   0.709289                 0.032023                0.005865           0.709289            1       True         11\n",
      "12       KNeighborsUnif    0.662069   0.694545        0.010096       0.012658   0.421139                 0.010096                0.012658           0.421139            1       True          1\n",
      "13       KNeighborsDist    0.662031   0.693431        0.009866       0.010300   0.006185                 0.009866                0.010300           0.006185            1       True          2\n"
     ]
    }
   ],
   "source": [
    "logging.basicConfig(level=logging.INFO)\n",
    "logger = logging.getLogger()\n",
    "\n",
    "\n",
    "target_column = \"binary_rating\"\n",
    "eval_metric = \"f1\"\n",
    "output_path_ag = \"ag-results\"\n",
    "\n",
    "train_data = TabularDataset(\"ag_processed_data/train_data.csv\")\n",
    "test_data = TabularDataset(\"ag_processed_data/test_data.csv\")\n",
    "\n",
    "ag_predictor_args = {\n",
    "    \"label\": target_column,\n",
    "    \"path\": output_path_ag,\n",
    "    \"eval_metric\": eval_metric,\n",
    "\n",
    "}\n",
    "\n",
    "predictor = TabularPredictor(**ag_predictor_args).fit(train_data)\n",
    "\n",
    "logger.info(\"Best model: %s\", predictor.get_model_best())\n",
    "\n",
    "# Leaderboard\n",
    "lb = predictor.leaderboard(test_data)\n",
    "lb.to_csv(f'{output_path_ag}/leaderboard.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d35ffc7",
   "metadata": {},
   "source": [
    "### Perform inference on the model trained by AutoGluon Model\n",
    "\n",
    "We assess the performance of the tabular model on the test data set. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "680eb41b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model = TabularPredictor.load(model_dir)\n",
    "globals()[\"column_names\"] = predictor.feature_metadata_in.get_features()\n",
    "\n",
    "ag_pred_prob = predictor.predict_proba(ag_test_df.drop([target_column], axis=1)).values\n",
    "ag_pred_prob = np.array(ag_pred_prob)\n",
    "ag_pred = np.where(ag_pred_prob[:,1] > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735dd089-def7-4146-bca6-ca49a6d5a490",
   "metadata": {},
   "source": [
    "### Compute evaluation metrics on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78f3b0ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, classification_report, matthews_corrcoef, balanced_accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34680a3e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ag_y_true = ag_test_df[target_column].values\n",
    "metrics_results = classification_report(ag_y_true, ag_pred, zero_division=1, output_dict=True)\n",
    "ag_results = pd.DataFrame(\n",
    "    {\n",
    "        \"F1 Score\": metrics_results[\"1\"][\"f1-score\"],\n",
    "        \"ROC AUC\": roc_auc_score(ag_y_true, ag_pred_prob[:, 1]),\n",
    "        \"Accuracy\": metrics_results[\"accuracy\"],\n",
    "        \"MCC\": matthews_corrcoef(ag_y_true, ag_pred),\n",
    "        \"Balanced Accuracy\": balanced_accuracy_score(ag_y_true, ag_pred),\n",
    "        \"Precision\": metrics_results[\"1\"][\"precision\"],\n",
    "        \"Recall\": metrics_results[\"1\"][\"recall\"],        \n",
    "    },\n",
    "    index=[\"AutoGluon\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e8f967c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AutoGluon</th>\n",
       "      <td>0.719251</td>\n",
       "      <td>0.749468</td>\n",
       "      <td>0.680365</td>\n",
       "      <td>0.352409</td>\n",
       "      <td>0.67349</td>\n",
       "      <td>0.686224</td>\n",
       "      <td>0.755618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           F1 Score   ROC AUC  Accuracy       MCC  Balanced Accuracy  \\\n",
       "AutoGluon  0.719251  0.749468  0.680365  0.352409            0.67349   \n",
       "\n",
       "           Precision    Recall  \n",
       "AutoGluon   0.686224  0.755618  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8f36d9",
   "metadata": {},
   "source": [
    "## Part 5: A Brief Introduction to Graph Machine Learning\n",
    "\n",
    "We assume that you have some knowledge of graph theory. The summary below is a brief description of graph ML. \n",
    "\n",
    "1. Machine learning using graph data is a broad subject and good expositions are available in books by Hamilton (2020); Blumauer and Nagy (2020); Ma and Tang (2021). (References below.) \n",
    "\n",
    "2. Graph machine learning requires representing the information on graphs as graph embeddings, of which a particular type is node embeddings. We can use deep learning methods to construct quality node embeddings using algorithms like [node2vec](https://snap.stanford.edu/node2vec/). You may also construct embeddings for links in a graph and also embeddings for entire graphs. \n",
    "\n",
    "3. Neural networks are used for classification with graph convolutional network (GCN) layers that use the normalized graph adjacency matrix with activation functions (e.g., ReLU) to incorporate network relationships into deep graph learning (Kipf and Welling, 2017). \n",
    "\n",
    "4. A graph (or network) $G$ comprises nodes or vertexes (set $V$, of size $|V|=n$) and links or edges (set $E$, of size $|E|=m$) and hence may be written as $G(V,E)$. The edge set may be (i) directed or undirected, (ii) weighted or unweighted. The graph may have single node types and single edge types, in which case it is denoted as \"homogeneous\" graph, but if it has multiple types of nodes or links, then it is known as a \"heterogeneous\" graph. For example, in a lending network, some nodes may be borrowers, others lenders, and some both. The number of links each node has is known as the \"degree\" of the node, and the distribution of number of links across all nodes is called the \"degree distribution\" of the graph, shown earlier. Our solution here is based on nodes that are of one type, companies, and hence uses a homogeneous graph.  \n",
    "\n",
    "5. The links in a homogeneous graph are often represented by an \"adjacency matrix\" $A$ of size $n \\times n$, which represents all links in the graph with non-zero values, such that a link from node $i$ to node $j$ is designated by $A_{ij}>0$. For an undirected graph, matrix $A$ is symmetric, i.e., $A_{ij}=A_{ji}$. An alternate data structure for a graph is a links table $L$ of size $m \\times 3$, with one row for each link from node $i$ to node $j$ with edge weight $w$, which we can denote as $L(i,j,w)$. The links table is a parsimonious data structure compared to the adjacency matrix, and for large (and sparse) graphs, is much more memory efficient.\n",
    "\n",
    "6. Node representations (embeddings) are vectors in $d$-dimensional embedding space. Algorithms to generate these embeddings aim to map similarity of nodes in the graph to closeness in the embedding vector space. Let the $d$-dimensional embedding vectors for nodes $i,j$ be denoted as $z_i, z_j \\in {\\cal R}^d$. We try to find vectors that map into the values in the adjacency matrix $A$, i.e., minimize loss ${\\cal L} = \\sum_i \\sum_j \\parallel z_i^\\top \\cdot z_j - A_{ij} \\parallel^2$.\n",
    "\n",
    "7. GCNs are trained to produce node, link, and graph embeddings. Node embeddings are a function of the features set of the node and its nearest neighbors, which may be a single hop away to many hops away (Cao, Lu, Xu (2015); Wu et al (2019)), a modeling choice, and have fixed length, dimension $d$. See Perozzi et al. (2014) for an efficient approach, known as `DeepWalk`, improved upon with biased random walks in the node2vec algorithm of Grover and Leskovec (2016). \n",
    "\n",
    "8. These *shallow* embeddings discussed above ignore data available on node features. They also cannot generate embeddings for new nodes that were not part of the original training of node embeddings. Better {\\it deep} encodings for nodes is possible with deep learning, using graph neural nets (GNNs). For a detailed exposition, see Scarselli et al (2009), a brief overview is as follows. Assume $k$ features at each of the $n$ nodes, resulting in a feature table $X \\in {\\cal R}^{k \\times n}$, with vector $x_i$ being the feature vector for node $i$. In our setting, these are all the variables that define the credit risk of a firm. The approach to generate embeddings is as follows. Assume a fixed neighborhood of a node (say 3 hops) and look at the 3-level tree below the chosen node. Suppose the node $i$ is connected to 4 nodes, each of which is connected to $1,2,4,3$ nodes respectively, and each of these nodes is also connected to more nodes (the exact number of connections is not important and the numbers here are merely illustrative, these links may contain cycles and thus include recursion). Assuming we have a representation for each of the 4 nodes connected to node $i$ already, then we can average these vectors, feed them into a neural network ${\\cal N}$ where the network generates a final output of embedding size $d$ as a node embedding for node $i$. Note that this approach from one hop away, can be recursively extended to the next 2 hops as well, ending with (in this case) 3 hops away (the leaves of the tree are the 3-hop nodes). At the last level, the inputs are the $k$-dimensional feature vectors for each node. These get transformed by weighting matrices in the vectors as we go up the tree to end up with the final embedding for node $i$. The node-level generated embeddings can be used with loss functions for similarity as discussed before in unsupervised mode, or on a node classification task in supervised mode. Once the neural net has been trained, you may generate embeddings for new nodes as they are added to the graph. This was not possible with the simpler embeddings discussed in the preceding paragraph.  \n",
    "\n",
    "9. So far, input vectors from the lower levels of a subtree emanating from a target node have been aggregated by weighted averaging of the inputs of neighbor nodes along with the embedding of the target node. Here, we use DGL, which implements a node embedding scheme known as [GraphSAGE](http://snap.stanford.edu/graphsage/). This approach extends neighborhood aggregation to concatenating the target node embedding with a function of the neighborhood node embeddings that may be simply a mean (pooling), or other forms of pooling, as well as applying more complex activations such as LSTMs using graph convolutional networks (GCNs). We will use these GCNs below. \n",
    "\n",
    "10. The embeddings and node features are now used to learn a classification neural net. \n",
    "\n",
    "### References\n",
    "\n",
    "- Cao, Shaosheng, Wei Lu, and Qiongkai Xu. “GraRep: Learning Graph Representations with Global Structural Information.” In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 891–900. CIKM ’15. New York, NY, USA: Association for Computing Machinery, 2015. https://doi.org/10.1145/2806416.2806512.\n",
    "- Grover, Aditya, and Jure Leskovec. “Node2vec: Scalable Feature Learning for Networks.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 855–64. KDD ’16. New York, NY, USA: Association for Computing Machinery, 2016. https://doi.org/10.1145/2939672.2939754.\n",
    "- Hamilton, William L. Graph Representation Learning. San Rafael, California: Morgan & Claypool, 2020.\n",
    "- Blumauer, Andreas, and Helmut Nagy. The Knowledge Graph Cookbook. edition mono/monochrom, 2020.\n",
    "- Ma, Yao, and Jiliang Tang. Deep Learning on Graphs. Cambridge: Cambridge University Press, 2021.\n",
    "- Kipf, Thomas N., and Max Welling. “Semi-Supervised Classification with Graph Convolutional Networks.” In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. https://openreview.net/forum?id=SJU4ayYgl.\n",
    "- Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. “DeepWalk: Online Learning of Social Representations.” In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 701–10. KDD ’14. New York, NY, USA: Association for Computing Machinery, 2014. https://doi.org/10.1145/2623330.2623732.\n",
    "- Scarselli, F., M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini (2009, January). \"The graph neural\n",
    "network model.\" IEEE transactions on neural networks 20 (1), 61–80. https://dl.acm.org/doi/10.1109/TNN.2008.2005605\n",
    "- Wu, Felix, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. “Simplifying Graph Convolutional Networks.” ArXiv:1902.07153 [Cs, Stat], June 20, 2019. http://arxiv.org/abs/1902.07153."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c2bf62",
   "metadata": {},
   "source": [
    "## Part 6: Train a Graph Convolutional Network with DGL using SageMaker Pytorch Container\n",
    "\n",
    "We use an adaptation of the GraphSAGE model (http://snap.stanford.edu/graphsage/) in conjunction with the Deep Graph Library (DGL, https://www.dgl.ai/). The steps are as follows:\n",
    "\n",
    "1. Read in graph data from S3 and create the source and destination node lists for `CorpNet`. \n",
    "2. Read in the graph node feature sets (train and test).\n",
    "3. Set tunable hyperparameters. \n",
    "4. Call the specialized graph machine learning container running PyTorch to fit the GNN without hyperparameter optimization (HPO).\n",
    "5. Redo graph ML with HPO."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6e3821",
   "metadata": {},
   "source": [
    "Save the model to the local directory and upload them to your S3 bucket to be ready for predictive use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d74bf779",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read in graph data\n",
    "src_dst_df = pd.DataFrame.from_dict(src_dst_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e15ca51a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(341226, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_dst_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d4815e32",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "col_to_normalization = [\"CurrentLiabs\", \"TotalLiabs\", \"RetainedEarnings\", \"CurrentAssets\", \"NetSales\", \"MktValueEquity\"]\n",
    "col_to_onehot_encoded = [\"industry_code\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb5c340",
   "metadata": {},
   "source": [
    "First, we normalize the numerical columns for both train and test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89e6ca98",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "train2normalize = train_df[col_to_normalization].values\n",
    "scaler.fit(train2normalize)\n",
    "normalized_train = scaler.transform(train2normalize)\n",
    "train_df[col_to_normalization] = normalized_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd06ccdc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "normalized_test = scaler.transform(test_df[col_to_normalization].values)\n",
    "test_df[col_to_normalization] = normalized_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67cac64a",
   "metadata": {},
   "source": [
    "Next, one-hot encode the categorical column for both train and test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "edf9adf3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "enc = OneHotEncoder(handle_unknown='error')\n",
    "train2enc = train_df[col_to_onehot_encoded].values\n",
    "enc.fit(train2enc)\n",
    "encoded_train = enc.transform(train2enc).toarray()\n",
    "\n",
    "enc_col_names = []\n",
    "for each in zip(col_to_onehot_encoded, enc.categories_):\n",
    "    enc_col_names += [each[0]+f\"_{x}\" for x in each[1]]\n",
    "train_df[enc_col_names] = encoded_train\n",
    "train_df.drop(col_to_onehot_encoded, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6ef49f4b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test2enc = test_df[col_to_onehot_encoded].values\n",
    "encoded_test = enc.transform(test2enc).toarray()\n",
    "test_df[enc_col_names] = encoded_test\n",
    "test_df.drop(col_to_onehot_encoded, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "335d7b5e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir -p gcn_processed_data\n",
    "src_dst_df.to_csv(\"gcn_processed_data/src_dst_df.csv\", header=True, index=False)\n",
    "train_df.to_csv(\"gcn_processed_data/features_train.csv\", header=True, index=True) # make sure the index is saved for distinguishing the train, validation, and test data. For details, see GCN/train_dgl_pytorch_entry_point.py\n",
    "test_df.to_csv(\"gcn_processed_data/features_test.csv\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d4b74a",
   "metadata": {},
   "source": [
    "### Set the initial hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68e2dbd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"n_hidden\": 32,\n",
    "    \"n_layers\": 2,\n",
    "    \"dropout\": 0.0,\n",
    "    \"weight_decay\": 0.000294, #0.00001,\n",
    "    \"n_epochs\": 119,#120,\n",
    "    \"lr\": 0.02, #0.0179,\n",
    "    \"aggregator_type\": \"pool\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c116d61",
   "metadata": {},
   "source": [
    "### Model training\n",
    "\n",
    "We run the model training using the open source Deep Graph Library (https://www.dgl.ai/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "61610038",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting dgl\n",
      "  Downloading dgl-1.0.0-cp38-cp38-manylinux1_x86_64.whl (5.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m76.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.8/site-packages (1.12.1+cpu)\n",
      "Requirement already satisfied: numpy>=1.14.0 in /opt/conda/lib/python3.8/site-packages (from dgl) (1.24.2)\n",
      "Requirement already satisfied: psutil>=5.8.0 in /opt/conda/lib/python3.8/site-packages (from dgl) (5.9.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.8/site-packages (from dgl) (2.28.2)\n",
      "Requirement already satisfied: networkx>=2.1 in /opt/conda/lib/python3.8/site-packages (from dgl) (3.0)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from dgl) (4.64.1)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.8/site-packages (from dgl) (1.10.0)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.8/site-packages (from torch) (4.4.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->dgl) (1.26.14)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->dgl) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->dgl) (3.0.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->dgl) (2022.12.7)\n",
      "Installing collected packages: dgl\n",
      "Successfully installed dgl-1.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install dgl torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f3e578a-95dd-4afc-ae4e-601f402a4c45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from gcn_local.train_dgl_pytorch_entry_point_local import entry_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2fceac6b-dbb1-4e16-afc5-de1f25c77774",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir -p gcn_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a54f37c-d2c1-47a7-bf63-a7d02e70039b",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa00508bcf0>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.1115 | Training Loss 15.7437 | Training F1 0.7025 | Validation Accuracy 0.5494 | Validation F1 0.7063\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0719 | Training Loss 1.8913 | Training F1 0.1204 | Validation Accuracy 0.4506 | Validation F1 0.0767\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0582 | Training Loss 1.1787 | Training F1 0.6808 | Validation Accuracy 0.5057 | Validation F1 0.6561\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0514 | Training Loss 0.8794 | Training F1 0.6987 | Validation Accuracy 0.5646 | Validation F1 0.7038\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0473 | Training Loss 0.8476 | Training F1 0.3849 | Validation Accuracy 0.5038 | Validation F1 0.3359\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0445 | Training Loss 0.7771 | Training F1 0.5342 | Validation Accuracy 0.5856 | Validation F1 0.5302\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0425 | Training Loss 0.7009 | Training F1 0.7102 | Validation Accuracy 0.6065 | Validation F1 0.7244\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0410 | Training Loss 0.7329 | Training F1 0.7222 | Validation Accuracy 0.6388 | Validation F1 0.7222\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0404 | Training Loss 0.6552 | Training F1 0.5906 | Validation Accuracy 0.6483 | Validation F1 0.5969\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0395 | Training Loss 0.6336 | Training F1 0.6404 | Validation Accuracy 0.6844 | Validation F1 0.6527\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0386 | Training Loss 0.6074 | Training F1 0.7488 | Validation Accuracy 0.7281 | Validation F1 0.7837\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0380 | Training Loss 0.5862 | Training F1 0.7576 | Validation Accuracy 0.7414 | Validation F1 0.7958\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0374 | Training Loss 0.5785 | Training F1 0.7176 | Validation Accuracy 0.7319 | Validation F1 0.7324\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0369 | Training Loss 0.5635 | Training F1 0.7292 | Validation Accuracy 0.7510 | Validation F1 0.7570\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0365 | Training Loss 0.5549 | Training F1 0.7663 | Validation Accuracy 0.7624 | Validation F1 0.8056\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0361 | Training Loss 0.5507 | Training F1 0.7683 | Validation Accuracy 0.7624 | Validation F1 0.7981\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0358 | Training Loss 0.5368 | Training F1 0.7339 | Validation Accuracy 0.7757 | Validation F1 0.7782\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0355 | Training Loss 0.5434 | Training F1 0.7660 | Validation Accuracy 0.7738 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0352 | Training Loss 0.5251 | Training F1 0.7743 | Validation Accuracy 0.7567 | Validation F1 0.7987\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0350 | Training Loss 0.5336 | Training F1 0.7574 | Validation Accuracy 0.7776 | Validation F1 0.7929\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0347 | Training Loss 0.5219 | Training F1 0.7554 | Validation Accuracy 0.7776 | Validation F1 0.7899\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0346 | Training Loss 0.5221 | Training F1 0.7769 | Validation Accuracy 0.7757 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0346 | Training Loss 0.5215 | Training F1 0.7702 | Validation Accuracy 0.7833 | Validation F1 0.8055\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0344 | Training Loss 0.5128 | Training F1 0.7499 | Validation Accuracy 0.7833 | Validation F1 0.7927\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0342 | Training Loss 0.5206 | Training F1 0.7786 | Validation Accuracy 0.7643 | Validation F1 0.8044\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0341 | Training Loss 0.5245 | Training F1 0.7745 | Validation Accuracy 0.7890 | Validation F1 0.8122\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0340 | Training Loss 0.5068 | Training F1 0.7471 | Validation Accuracy 0.7814 | Validation F1 0.7866\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0338 | Training Loss 0.5192 | Training F1 0.7828 | Validation Accuracy 0.7871 | Validation F1 0.8146\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0337 | Training Loss 0.5036 | Training F1 0.7850 | Validation Accuracy 0.7757 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0336 | Training Loss 0.5081 | Training F1 0.7665 | Validation Accuracy 0.7928 | Validation F1 0.8071\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0335 | Training Loss 0.5021 | Training F1 0.7682 | Validation Accuracy 0.7947 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0334 | Training Loss 0.4984 | Training F1 0.7874 | Validation Accuracy 0.7852 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0333 | Training Loss 0.4997 | Training F1 0.7880 | Validation Accuracy 0.7928 | Validation F1 0.8156\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0332 | Training Loss 0.4899 | Training F1 0.7674 | Validation Accuracy 0.7890 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0331 | Training Loss 0.4959 | Training F1 0.7931 | Validation Accuracy 0.7833 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0330 | Training Loss 0.4883 | Training F1 0.7958 | Validation Accuracy 0.7909 | Validation F1 0.8208\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0330 | Training Loss 0.4847 | Training F1 0.7743 | Validation Accuracy 0.7966 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0329 | Training Loss 0.4870 | Training F1 0.7959 | Validation Accuracy 0.7871 | Validation F1 0.8133\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0328 | Training Loss 0.4778 | Training F1 0.7975 | Validation Accuracy 0.7814 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0328 | Training Loss 0.4795 | Training F1 0.7858 | Validation Accuracy 0.7966 | Validation F1 0.8113\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0329 | Training Loss 0.4768 | Training F1 0.7986 | Validation Accuracy 0.7928 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0329 | Training Loss 0.4704 | Training F1 0.7973 | Validation Accuracy 0.7776 | Validation F1 0.8104\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0328 | Training Loss 0.4722 | Training F1 0.7948 | Validation Accuracy 0.7966 | Validation F1 0.8139\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0327 | Training Loss 0.4684 | Training F1 0.8035 | Validation Accuracy 0.7985 | Validation F1 0.8185\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0327 | Training Loss 0.4635 | Training F1 0.7995 | Validation Accuracy 0.7947 | Validation F1 0.8224\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0326 | Training Loss 0.4646 | Training F1 0.8005 | Validation Accuracy 0.7890 | Validation F1 0.8056\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0326 | Training Loss 0.4625 | Training F1 0.8055 | Validation Accuracy 0.8042 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0325 | Training Loss 0.4573 | Training F1 0.8043 | Validation Accuracy 0.7966 | Validation F1 0.8202\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0325 | Training Loss 0.4554 | Training F1 0.8000 | Validation Accuracy 0.7966 | Validation F1 0.8120\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0324 | Training Loss 0.4562 | Training F1 0.8070 | Validation Accuracy 0.7947 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0324 | Training Loss 0.4557 | Training F1 0.7998 | Validation Accuracy 0.8023 | Validation F1 0.8169\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0323 | Training Loss 0.4518 | Training F1 0.8103 | Validation Accuracy 0.8023 | Validation F1 0.8272\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0323 | Training Loss 0.4478 | Training F1 0.8099 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0323 | Training Loss 0.4452 | Training F1 0.8044 | Validation Accuracy 0.7985 | Validation F1 0.8172\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0322 | Training Loss 0.4442 | Training F1 0.8171 | Validation Accuracy 0.8061 | Validation F1 0.8317\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0322 | Training Loss 0.4446 | Training F1 0.7925 | Validation Accuracy 0.8004 | Validation F1 0.8142\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0321 | Training Loss 0.4468 | Training F1 0.8082 | Validation Accuracy 0.7871 | Validation F1 0.8217\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0321 | Training Loss 0.4528 | Training F1 0.7779 | Validation Accuracy 0.7852 | Validation F1 0.7896\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0321 | Training Loss 0.4569 | Training F1 0.8071 | Validation Accuracy 0.7795 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0320 | Training Loss 0.4592 | Training F1 0.7894 | Validation Accuracy 0.7985 | Validation F1 0.8087\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0320 | Training Loss 0.4461 | Training F1 0.8199 | Validation Accuracy 0.8137 | Validation F1 0.8361\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0320 | Training Loss 0.4348 | Training F1 0.8191 | Validation Accuracy 0.8099 | Validation F1 0.8344\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0319 | Training Loss 0.4347 | Training F1 0.7935 | Validation Accuracy 0.8004 | Validation F1 0.8115\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0319 | Training Loss 0.4408 | Training F1 0.8158 | Validation Accuracy 0.8042 | Validation F1 0.8347\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0319 | Training Loss 0.4431 | Training F1 0.7985 | Validation Accuracy 0.8023 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0319 | Training Loss 0.4348 | Training F1 0.8156 | Validation Accuracy 0.8137 | Validation F1 0.8350\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0319 | Training Loss 0.4280 | Training F1 0.8172 | Validation Accuracy 0.8080 | Validation F1 0.8314\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0318 | Training Loss 0.4277 | Training F1 0.8002 | Validation Accuracy 0.8004 | Validation F1 0.8122\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0318 | Training Loss 0.4322 | Training F1 0.8207 | Validation Accuracy 0.7985 | Validation F1 0.8307\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0318 | Training Loss 0.4404 | Training F1 0.7927 | Validation Accuracy 0.7985 | Validation F1 0.8051\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0317 | Training Loss 0.4393 | Training F1 0.8201 | Validation Accuracy 0.8099 | Validation F1 0.8377\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0317 | Training Loss 0.4308 | Training F1 0.8174 | Validation Accuracy 0.8099 | Validation F1 0.8288\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0318 | Training Loss 0.4219 | Training F1 0.8115 | Validation Accuracy 0.8042 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0319 | Training Loss 0.4227 | Training F1 0.8206 | Validation Accuracy 0.8118 | Validation F1 0.8395\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0319 | Training Loss 0.4284 | Training F1 0.7981 | Validation Accuracy 0.8061 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0319 | Training Loss 0.4278 | Training F1 0.8207 | Validation Accuracy 0.8004 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0319 | Training Loss 0.4221 | Training F1 0.8187 | Validation Accuracy 0.8118 | Validation F1 0.8313\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0320 | Training Loss 0.4167 | Training F1 0.8124 | Validation Accuracy 0.8080 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0320 | Training Loss 0.4175 | Training F1 0.8235 | Validation Accuracy 0.8080 | Validation F1 0.8342\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0321 | Training Loss 0.4215 | Training F1 0.8043 | Validation Accuracy 0.8023 | Validation F1 0.8116\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0321 | Training Loss 0.4226 | Training F1 0.8233 | Validation Accuracy 0.8080 | Validation F1 0.8342\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0321 | Training Loss 0.4198 | Training F1 0.8126 | Validation Accuracy 0.8099 | Validation F1 0.8258\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0321 | Training Loss 0.4144 | Training F1 0.8232 | Validation Accuracy 0.8099 | Validation F1 0.8299\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0320 | Training Loss 0.4113 | Training F1 0.8259 | Validation Accuracy 0.8099 | Validation F1 0.8311\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0320 | Training Loss 0.4111 | Training F1 0.8130 | Validation Accuracy 0.8156 | Validation F1 0.8301\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0320 | Training Loss 0.4123 | Training F1 0.8276 | Validation Accuracy 0.8004 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0320 | Training Loss 0.4137 | Training F1 0.8105 | Validation Accuracy 0.8023 | Validation F1 0.8143\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0319 | Training Loss 0.4140 | Training F1 0.8270 | Validation Accuracy 0.8042 | Validation F1 0.8298\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0320 | Training Loss 0.4132 | Training F1 0.8125 | Validation Accuracy 0.8042 | Validation F1 0.8164\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0321 | Training Loss 0.4117 | Training F1 0.8292 | Validation Accuracy 0.8023 | Validation F1 0.8272\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0321 | Training Loss 0.4097 | Training F1 0.8179 | Validation Accuracy 0.8061 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0321 | Training Loss 0.4076 | Training F1 0.8308 | Validation Accuracy 0.8023 | Validation F1 0.8267\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0321 | Training Loss 0.4057 | Training F1 0.8227 | Validation Accuracy 0.8061 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0321 | Training Loss 0.4040 | Training F1 0.8298 | Validation Accuracy 0.8004 | Validation F1 0.8217\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0321 | Training Loss 0.4027 | Training F1 0.8277 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0321 | Training Loss 0.4016 | Training F1 0.8304 | Validation Accuracy 0.8042 | Validation F1 0.8227\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0321 | Training Loss 0.4007 | Training F1 0.8305 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0321 | Training Loss 0.4001 | Training F1 0.8262 | Validation Accuracy 0.8023 | Validation F1 0.8207\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0321 | Training Loss 0.3996 | Training F1 0.8327 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0341 | Training Loss 0.3995 | Training F1 0.8181 | Validation Accuracy 0.8099 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0414 | Training Loss 0.4011 | Training F1 0.8302 | Validation Accuracy 0.8023 | Validation F1 0.8306\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0427 | Training Loss 0.4089 | Training F1 0.7821 | Validation Accuracy 0.7852 | Validation F1 0.7797\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0428 | Training Loss 0.4357 | Training F1 0.8070 | Validation Accuracy 0.7224 | Validation F1 0.7896\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0428 | Training Loss 0.5148 | Training F1 0.6761 | Validation Accuracy 0.7338 | Validation F1 0.6916\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0427 | Training Loss 0.5557 | Training F1 0.8089 | Validation Accuracy 0.7262 | Validation F1 0.7901\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0427 | Training Loss 0.4991 | Training F1 0.8341 | Validation Accuracy 0.8061 | Validation F1 0.8253\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0427 | Training Loss 0.3962 | Training F1 0.7521 | Validation Accuracy 0.7643 | Validation F1 0.7449\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0426 | Training Loss 0.4784 | Training F1 0.8195 | Validation Accuracy 0.7586 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0425 | Training Loss 0.4532 | Training F1 0.8383 | Validation Accuracy 0.7966 | Validation F1 0.8254\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0425 | Training Loss 0.4084 | Training F1 0.7535 | Validation Accuracy 0.7624 | Validation F1 0.7475\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0424 | Training Loss 0.4684 | Training F1 0.8354 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0423 | Training Loss 0.3990 | Training F1 0.8209 | Validation Accuracy 0.7833 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0422 | Training Loss 0.4424 | Training F1 0.8084 | Validation Accuracy 0.8004 | Validation F1 0.8108\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0423 | Training Loss 0.4054 | Training F1 0.7948 | Validation Accuracy 0.7928 | Validation F1 0.7932\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0422 | Training Loss 0.4220 | Training F1 0.8334 | Validation Accuracy 0.8080 | Validation F1 0.8374\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0422 | Training Loss 0.4115 | Training F1 0.8353 | Validation Accuracy 0.8137 | Validation F1 0.8414\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0421 | Training Loss 0.4082 | Training F1 0.8044 | Validation Accuracy 0.8023 | Validation F1 0.8088\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0421 | Training Loss 0.4125 | Training F1 0.8143 | Validation Accuracy 0.8118 | Validation F1 0.8242\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0420 | Training Loss 0.3993 | Training F1 0.8320 | Validation Accuracy 0.8118 | Validation F1 0.8406\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa002a357b0>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0430 | Training Loss 2.3607 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0383 | Training Loss 35.3433 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0382 | Training Loss 21.6900 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0448 | Training Loss 7.4113 | Training F1 0.7023 | Validation Accuracy 0.5437 | Validation F1 0.7022\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0425 | Training Loss 2.0959 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0434 | Training Loss 3.8723 | Training F1 0.7025 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0429 | Training Loss 3.1130 | Training F1 0.7070 | Validation Accuracy 0.5494 | Validation F1 0.7063\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0424 | Training Loss 1.6418 | Training F1 0.6904 | Validation Accuracy 0.6445 | Validation F1 0.7110\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0415 | Training Loss 0.7767 | Training F1 0.3475 | Validation Accuracy 0.5608 | Validation F1 0.3937\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0407 | Training Loss 0.8609 | Training F1 0.1237 | Validation Accuracy 0.4943 | Validation F1 0.1635\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0398 | Training Loss 0.9369 | Training F1 0.1685 | Validation Accuracy 0.4924 | Validation F1 0.1734\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0400 | Training Loss 0.7603 | Training F1 0.7023 | Validation Accuracy 0.6179 | Validation F1 0.6968\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0396 | Training Loss 0.6483 | Training F1 0.7125 | Validation Accuracy 0.5684 | Validation F1 0.7137\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0400 | Training Loss 0.7152 | Training F1 0.7174 | Validation Accuracy 0.5894 | Validation F1 0.7224\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0404 | Training Loss 0.6881 | Training F1 0.6999 | Validation Accuracy 0.6787 | Validation F1 0.7305\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0402 | Training Loss 0.6287 | Training F1 0.4275 | Validation Accuracy 0.5894 | Validation F1 0.4600\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0399 | Training Loss 0.6938 | Training F1 0.4632 | Validation Accuracy 0.5970 | Validation F1 0.4904\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0394 | Training Loss 0.6876 | Training F1 0.6643 | Validation Accuracy 0.6483 | Validation F1 0.6848\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0390 | Training Loss 0.6382 | Training F1 0.7229 | Validation Accuracy 0.6103 | Validation F1 0.7256\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0386 | Training Loss 0.6586 | Training F1 0.7173 | Validation Accuracy 0.5951 | Validation F1 0.7223\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0382 | Training Loss 0.6795 | Training F1 0.7265 | Validation Accuracy 0.6179 | Validation F1 0.7280\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0379 | Training Loss 0.6489 | Training F1 0.6832 | Validation Accuracy 0.6654 | Validation F1 0.7047\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0376 | Training Loss 0.6287 | Training F1 0.5792 | Validation Accuracy 0.6559 | Validation F1 0.6314\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0373 | Training Loss 0.6454 | Training F1 0.5926 | Validation Accuracy 0.6635 | Validation F1 0.6380\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0371 | Training Loss 0.6402 | Training F1 0.6847 | Validation Accuracy 0.6844 | Validation F1 0.7098\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0368 | Training Loss 0.6171 | Training F1 0.7331 | Validation Accuracy 0.6635 | Validation F1 0.7424\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0366 | Training Loss 0.6185 | Training F1 0.7373 | Validation Accuracy 0.6312 | Validation F1 0.7313\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0364 | Training Loss 0.6280 | Training F1 0.7357 | Validation Accuracy 0.6578 | Validation F1 0.7399\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0364 | Training Loss 0.6160 | Training F1 0.7155 | Validation Accuracy 0.7091 | Validation F1 0.7463\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0362 | Training Loss 0.6020 | Training F1 0.6589 | Validation Accuracy 0.6958 | Validation F1 0.6911\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0362 | Training Loss 0.6092 | Training F1 0.6717 | Validation Accuracy 0.7110 | Validation F1 0.7143\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0361 | Training Loss 0.6024 | Training F1 0.7173 | Validation Accuracy 0.7167 | Validation F1 0.7504\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0359 | Training Loss 0.5909 | Training F1 0.7371 | Validation Accuracy 0.6882 | Validation F1 0.7500\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0363 | Training Loss 0.5923 | Training F1 0.7419 | Validation Accuracy 0.6882 | Validation F1 0.7538\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0364 | Training Loss 0.5917 | Training F1 0.7338 | Validation Accuracy 0.6996 | Validation F1 0.7508\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0365 | Training Loss 0.5805 | Training F1 0.7176 | Validation Accuracy 0.7319 | Validation F1 0.7487\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0366 | Training Loss 0.5775 | Training F1 0.7043 | Validation Accuracy 0.7357 | Validation F1 0.7486\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0365 | Training Loss 0.5767 | Training F1 0.7293 | Validation Accuracy 0.7452 | Validation F1 0.7682\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0399 | Training Loss 0.5669 | Training F1 0.7451 | Validation Accuracy 0.7262 | Validation F1 0.7729\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0416 | Training Loss 0.5637 | Training F1 0.7499 | Validation Accuracy 0.7186 | Validation F1 0.7687\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0453 | Training Loss 0.5614 | Training F1 0.7449 | Validation Accuracy 0.7281 | Validation F1 0.7636\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0455 | Training Loss 0.5533 | Training F1 0.7281 | Validation Accuracy 0.7490 | Validation F1 0.7676\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0475 | Training Loss 0.5528 | Training F1 0.7323 | Validation Accuracy 0.7490 | Validation F1 0.7692\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0476 | Training Loss 0.5486 | Training F1 0.7501 | Validation Accuracy 0.7414 | Validation F1 0.7792\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0476 | Training Loss 0.5431 | Training F1 0.7590 | Validation Accuracy 0.7224 | Validation F1 0.7704\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0474 | Training Loss 0.5435 | Training F1 0.7512 | Validation Accuracy 0.7490 | Validation F1 0.7836\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0471 | Training Loss 0.5366 | Training F1 0.7412 | Validation Accuracy 0.7719 | Validation F1 0.7917\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0468 | Training Loss 0.5366 | Training F1 0.7439 | Validation Accuracy 0.7681 | Validation F1 0.7939\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0465 | Training Loss 0.5314 | Training F1 0.7632 | Validation Accuracy 0.7395 | Validation F1 0.7794\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0466 | Training Loss 0.5286 | Training F1 0.7615 | Validation Accuracy 0.7433 | Validation F1 0.7840\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0465 | Training Loss 0.5255 | Training F1 0.7576 | Validation Accuracy 0.7662 | Validation F1 0.7926\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0465 | Training Loss 0.5212 | Training F1 0.7564 | Validation Accuracy 0.7776 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0465 | Training Loss 0.5201 | Training F1 0.7655 | Validation Accuracy 0.7605 | Validation F1 0.7928\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0463 | Training Loss 0.5156 | Training F1 0.7677 | Validation Accuracy 0.7529 | Validation F1 0.7917\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0461 | Training Loss 0.5151 | Training F1 0.7671 | Validation Accuracy 0.7624 | Validation F1 0.7927\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0462 | Training Loss 0.5113 | Training F1 0.7688 | Validation Accuracy 0.7700 | Validation F1 0.7939\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0460 | Training Loss 0.5107 | Training F1 0.7706 | Validation Accuracy 0.7605 | Validation F1 0.7928\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0457 | Training Loss 0.5066 | Training F1 0.7760 | Validation Accuracy 0.7586 | Validation F1 0.7948\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0455 | Training Loss 0.5055 | Training F1 0.7745 | Validation Accuracy 0.7586 | Validation F1 0.7908\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0454 | Training Loss 0.5019 | Training F1 0.7785 | Validation Accuracy 0.7795 | Validation F1 0.8027\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0453 | Training Loss 0.5011 | Training F1 0.7798 | Validation Accuracy 0.7624 | Validation F1 0.7941\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0451 | Training Loss 0.4977 | Training F1 0.7773 | Validation Accuracy 0.7624 | Validation F1 0.7981\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0451 | Training Loss 0.4968 | Training F1 0.7861 | Validation Accuracy 0.7681 | Validation F1 0.7946\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0450 | Training Loss 0.4939 | Training F1 0.7843 | Validation Accuracy 0.7681 | Validation F1 0.7918\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0448 | Training Loss 0.4923 | Training F1 0.7815 | Validation Accuracy 0.7700 | Validation F1 0.8013\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0448 | Training Loss 0.4901 | Training F1 0.7879 | Validation Accuracy 0.7719 | Validation F1 0.8013\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0448 | Training Loss 0.4876 | Training F1 0.7847 | Validation Accuracy 0.7776 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0446 | Training Loss 0.4862 | Training F1 0.7863 | Validation Accuracy 0.7814 | Validation F1 0.8080\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0446 | Training Loss 0.4832 | Training F1 0.7874 | Validation Accuracy 0.7833 | Validation F1 0.8113\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0446 | Training Loss 0.4812 | Training F1 0.7907 | Validation Accuracy 0.7833 | Validation F1 0.8055\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0444 | Training Loss 0.4794 | Training F1 0.7924 | Validation Accuracy 0.7909 | Validation F1 0.8154\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0444 | Training Loss 0.4771 | Training F1 0.7973 | Validation Accuracy 0.7833 | Validation F1 0.8113\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0442 | Training Loss 0.4755 | Training F1 0.7914 | Validation Accuracy 0.7852 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0443 | Training Loss 0.4738 | Training F1 0.7985 | Validation Accuracy 0.7890 | Validation F1 0.8141\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0442 | Training Loss 0.4719 | Training F1 0.8007 | Validation Accuracy 0.7871 | Validation F1 0.8133\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0441 | Training Loss 0.4705 | Training F1 0.7947 | Validation Accuracy 0.7890 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0439 | Training Loss 0.4692 | Training F1 0.8000 | Validation Accuracy 0.7814 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0438 | Training Loss 0.4675 | Training F1 0.7993 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0436 | Training Loss 0.4657 | Training F1 0.7990 | Validation Accuracy 0.7852 | Validation F1 0.8088\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0435 | Training Loss 0.4645 | Training F1 0.8005 | Validation Accuracy 0.7795 | Validation F1 0.8086\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0434 | Training Loss 0.4637 | Training F1 0.7993 | Validation Accuracy 0.7909 | Validation F1 0.8116\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0432 | Training Loss 0.4631 | Training F1 0.8042 | Validation Accuracy 0.7738 | Validation F1 0.8065\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0432 | Training Loss 0.4626 | Training F1 0.7979 | Validation Accuracy 0.7852 | Validation F1 0.8042\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0431 | Training Loss 0.4617 | Training F1 0.8072 | Validation Accuracy 0.7700 | Validation F1 0.8033\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0429 | Training Loss 0.4608 | Training F1 0.7998 | Validation Accuracy 0.7795 | Validation F1 0.7979\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0428 | Training Loss 0.4597 | Training F1 0.8067 | Validation Accuracy 0.7719 | Validation F1 0.8039\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0426 | Training Loss 0.4583 | Training F1 0.8055 | Validation Accuracy 0.7871 | Validation F1 0.8069\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0425 | Training Loss 0.4566 | Training F1 0.8060 | Validation Accuracy 0.7814 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0423 | Training Loss 0.4547 | Training F1 0.8076 | Validation Accuracy 0.7814 | Validation F1 0.8067\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0422 | Training Loss 0.4531 | Training F1 0.8078 | Validation Accuracy 0.7852 | Validation F1 0.8088\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0421 | Training Loss 0.4520 | Training F1 0.8088 | Validation Accuracy 0.7776 | Validation F1 0.8066\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0419 | Training Loss 0.4516 | Training F1 0.8016 | Validation Accuracy 0.7871 | Validation F1 0.8042\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0418 | Training Loss 0.4520 | Training F1 0.8110 | Validation Accuracy 0.7757 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0417 | Training Loss 0.4555 | Training F1 0.7713 | Validation Accuracy 0.7852 | Validation F1 0.7888\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0415 | Training Loss 0.4681 | Training F1 0.8049 | Validation Accuracy 0.7376 | Validation F1 0.7915\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0414 | Training Loss 0.4982 | Training F1 0.7258 | Validation Accuracy 0.7662 | Validation F1 0.7515\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0413 | Training Loss 0.5098 | Training F1 0.8077 | Validation Accuracy 0.7567 | Validation F1 0.7994\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0412 | Training Loss 0.4633 | Training F1 0.8164 | Validation Accuracy 0.7776 | Validation F1 0.8110\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0411 | Training Loss 0.4526 | Training F1 0.7528 | Validation Accuracy 0.7814 | Validation F1 0.7776\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0410 | Training Loss 0.4796 | Training F1 0.8173 | Validation Accuracy 0.7757 | Validation F1 0.8066\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0408 | Training Loss 0.4486 | Training F1 0.8070 | Validation Accuracy 0.7662 | Validation F1 0.8075\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0408 | Training Loss 0.4620 | Training F1 0.7841 | Validation Accuracy 0.7966 | Validation F1 0.8037\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0407 | Training Loss 0.4579 | Training F1 0.8041 | Validation Accuracy 0.8023 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0406 | Training Loss 0.4482 | Training F1 0.8077 | Validation Accuracy 0.7681 | Validation F1 0.8082\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0405 | Training Loss 0.4601 | Training F1 0.8062 | Validation Accuracy 0.7947 | Validation F1 0.8169\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0404 | Training Loss 0.4431 | Training F1 0.7852 | Validation Accuracy 0.8023 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0403 | Training Loss 0.4556 | Training F1 0.8154 | Validation Accuracy 0.7833 | Validation F1 0.8113\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0402 | Training Loss 0.4426 | Training F1 0.8188 | Validation Accuracy 0.7738 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0401 | Training Loss 0.4490 | Training F1 0.8049 | Validation Accuracy 0.8004 | Validation F1 0.8128\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0400 | Training Loss 0.4449 | Training F1 0.8082 | Validation Accuracy 0.7928 | Validation F1 0.8078\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0399 | Training Loss 0.4412 | Training F1 0.8200 | Validation Accuracy 0.7776 | Validation F1 0.8122\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0399 | Training Loss 0.4466 | Training F1 0.8120 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0398 | Training Loss 0.4371 | Training F1 0.8063 | Validation Accuracy 0.7890 | Validation F1 0.8021\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0397 | Training Loss 0.4425 | Training F1 0.8164 | Validation Accuracy 0.7871 | Validation F1 0.8170\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0396 | Training Loss 0.4389 | Training F1 0.8153 | Validation Accuracy 0.7833 | Validation F1 0.8100\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0395 | Training Loss 0.4356 | Training F1 0.8083 | Validation Accuracy 0.7909 | Validation F1 0.8036\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0394 | Training Loss 0.4398 | Training F1 0.8148 | Validation Accuracy 0.7928 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0393 | Training Loss 0.4340 | Training F1 0.8145 | Validation Accuracy 0.7852 | Validation F1 0.8126\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0393 | Training Loss 0.4343 | Training F1 0.8099 | Validation Accuracy 0.7928 | Validation F1 0.8057\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa002a35580>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0414 | Training Loss 4.0093 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0362 | Training Loss 23.0841 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0344 | Training Loss 8.4786 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0335 | Training Loss 4.0382 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0329 | Training Loss 4.3723 | Training F1 0.7045 | Validation Accuracy 0.5494 | Validation F1 0.7041\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0325 | Training Loss 1.1736 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0321 | Training Loss 3.9951 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0319 | Training Loss 3.5709 | Training F1 0.0628 | Validation Accuracy 0.4696 | Validation F1 0.0478\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0317 | Training Loss 1.0936 | Training F1 0.7029 | Validation Accuracy 0.5475 | Validation F1 0.7054\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0315 | Training Loss 1.2926 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0322 | Training Loss 1.9482 | Training F1 0.7023 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0320 | Training Loss 1.7091 | Training F1 0.7085 | Validation Accuracy 0.5589 | Validation F1 0.7107\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0319 | Training Loss 1.0075 | Training F1 0.6555 | Validation Accuracy 0.6825 | Validation F1 0.6745\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0318 | Training Loss 0.6198 | Training F1 0.1397 | Validation Accuracy 0.4810 | Validation F1 0.1049\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0317 | Training Loss 1.0404 | Training F1 0.0856 | Validation Accuracy 0.4715 | Validation F1 0.0608\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0317 | Training Loss 1.1100 | Training F1 0.3133 | Validation Accuracy 0.5646 | Validation F1 0.3476\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0317 | Training Loss 0.8116 | Training F1 0.6943 | Validation Accuracy 0.7091 | Validation F1 0.7243\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0316 | Training Loss 0.5963 | Training F1 0.7225 | Validation Accuracy 0.6103 | Validation F1 0.7348\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0316 | Training Loss 0.7066 | Training F1 0.7124 | Validation Accuracy 0.5741 | Validation F1 0.7179\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0315 | Training Loss 0.7910 | Training F1 0.7131 | Validation Accuracy 0.5741 | Validation F1 0.7179\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0315 | Training Loss 0.7674 | Training F1 0.7293 | Validation Accuracy 0.6122 | Validation F1 0.7351\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0314 | Training Loss 0.6751 | Training F1 0.7398 | Validation Accuracy 0.6901 | Validation F1 0.7534\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0314 | Training Loss 0.5997 | Training F1 0.6188 | Validation Accuracy 0.6369 | Validation F1 0.6013\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0314 | Training Loss 0.6163 | Training F1 0.4866 | Validation Accuracy 0.6122 | Validation F1 0.4950\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0314 | Training Loss 0.6532 | Training F1 0.5393 | Validation Accuracy 0.6236 | Validation F1 0.5395\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0313 | Training Loss 0.6338 | Training F1 0.7314 | Validation Accuracy 0.6958 | Validation F1 0.7386\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0313 | Training Loss 0.6010 | Training F1 0.7224 | Validation Accuracy 0.5837 | Validation F1 0.7210\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0313 | Training Loss 0.6459 | Training F1 0.7563 | Validation Accuracy 0.7148 | Validation F1 0.7774\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0313 | Training Loss 0.5987 | Training F1 0.6162 | Validation Accuracy 0.6559 | Validation F1 0.6157\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0312 | Training Loss 0.6065 | Training F1 0.5734 | Validation Accuracy 0.6388 | Validation F1 0.5721\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0313 | Training Loss 0.6128 | Training F1 0.6820 | Validation Accuracy 0.6996 | Validation F1 0.6938\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0312 | Training Loss 0.5854 | Training F1 0.7537 | Validation Accuracy 0.7300 | Validation F1 0.7664\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0312 | Training Loss 0.5735 | Training F1 0.7640 | Validation Accuracy 0.7167 | Validation F1 0.7812\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0312 | Training Loss 0.5832 | Training F1 0.7677 | Validation Accuracy 0.7300 | Validation F1 0.7881\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0312 | Training Loss 0.5780 | Training F1 0.7556 | Validation Accuracy 0.7281 | Validation F1 0.7682\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0312 | Training Loss 0.5621 | Training F1 0.7405 | Validation Accuracy 0.7376 | Validation F1 0.7527\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0312 | Training Loss 0.5578 | Training F1 0.7180 | Validation Accuracy 0.7243 | Validation F1 0.7280\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0312 | Training Loss 0.5611 | Training F1 0.7192 | Validation Accuracy 0.7262 | Validation F1 0.7293\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0311 | Training Loss 0.5573 | Training F1 0.7419 | Validation Accuracy 0.7548 | Validation F1 0.7709\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0311 | Training Loss 0.5470 | Training F1 0.7597 | Validation Accuracy 0.7433 | Validation F1 0.7761\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0311 | Training Loss 0.5410 | Training F1 0.7733 | Validation Accuracy 0.7490 | Validation F1 0.7918\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0311 | Training Loss 0.5421 | Training F1 0.7730 | Validation Accuracy 0.7510 | Validation F1 0.7937\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0314 | Training Loss 0.5390 | Training F1 0.7712 | Validation Accuracy 0.7471 | Validation F1 0.7809\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0314 | Training Loss 0.5308 | Training F1 0.7523 | Validation Accuracy 0.7567 | Validation F1 0.7762\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0314 | Training Loss 0.5285 | Training F1 0.7461 | Validation Accuracy 0.7471 | Validation F1 0.7621\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0314 | Training Loss 0.5296 | Training F1 0.7556 | Validation Accuracy 0.7529 | Validation F1 0.7711\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0314 | Training Loss 0.5233 | Training F1 0.7736 | Validation Accuracy 0.7624 | Validation F1 0.7892\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0314 | Training Loss 0.5181 | Training F1 0.7775 | Validation Accuracy 0.7624 | Validation F1 0.7954\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0313 | Training Loss 0.5185 | Training F1 0.7795 | Validation Accuracy 0.7643 | Validation F1 0.7954\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0313 | Training Loss 0.5143 | Training F1 0.7757 | Validation Accuracy 0.7662 | Validation F1 0.7883\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0313 | Training Loss 0.5101 | Training F1 0.7705 | Validation Accuracy 0.7700 | Validation F1 0.7873\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0313 | Training Loss 0.5099 | Training F1 0.7762 | Validation Accuracy 0.7700 | Validation F1 0.7896\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0313 | Training Loss 0.5049 | Training F1 0.7805 | Validation Accuracy 0.7700 | Validation F1 0.7980\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0313 | Training Loss 0.5025 | Training F1 0.7799 | Validation Accuracy 0.7700 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0313 | Training Loss 0.5016 | Training F1 0.7778 | Validation Accuracy 0.7776 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0313 | Training Loss 0.4973 | Training F1 0.7785 | Validation Accuracy 0.7643 | Validation F1 0.7809\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0312 | Training Loss 0.4962 | Training F1 0.7759 | Validation Accuracy 0.7738 | Validation F1 0.7916\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0312 | Training Loss 0.4939 | Training F1 0.7797 | Validation Accuracy 0.7833 | Validation F1 0.8061\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0312 | Training Loss 0.4907 | Training F1 0.7886 | Validation Accuracy 0.7757 | Validation F1 0.8020\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0312 | Training Loss 0.4895 | Training F1 0.7818 | Validation Accuracy 0.7738 | Validation F1 0.7966\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0312 | Training Loss 0.4859 | Training F1 0.7753 | Validation Accuracy 0.7852 | Validation F1 0.8021\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0312 | Training Loss 0.4846 | Training F1 0.7782 | Validation Accuracy 0.7814 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0312 | Training Loss 0.4818 | Training F1 0.7889 | Validation Accuracy 0.7814 | Validation F1 0.8048\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0312 | Training Loss 0.4790 | Training F1 0.7891 | Validation Accuracy 0.7814 | Validation F1 0.8041\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0312 | Training Loss 0.4769 | Training F1 0.7871 | Validation Accuracy 0.7833 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0312 | Training Loss 0.4738 | Training F1 0.7862 | Validation Accuracy 0.7776 | Validation F1 0.7951\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0311 | Training Loss 0.4724 | Training F1 0.7903 | Validation Accuracy 0.7890 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0311 | Training Loss 0.4694 | Training F1 0.7908 | Validation Accuracy 0.7833 | Validation F1 0.8055\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0312 | Training Loss 0.4678 | Training F1 0.7933 | Validation Accuracy 0.7871 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0312 | Training Loss 0.4652 | Training F1 0.7939 | Validation Accuracy 0.7909 | Validation F1 0.8077\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0312 | Training Loss 0.4634 | Training F1 0.7995 | Validation Accuracy 0.7890 | Validation F1 0.8063\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0312 | Training Loss 0.4611 | Training F1 0.7995 | Validation Accuracy 0.7871 | Validation F1 0.8089\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0312 | Training Loss 0.4593 | Training F1 0.8022 | Validation Accuracy 0.7871 | Validation F1 0.8069\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0311 | Training Loss 0.4571 | Training F1 0.8017 | Validation Accuracy 0.7890 | Validation F1 0.8063\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0312 | Training Loss 0.4553 | Training F1 0.8024 | Validation Accuracy 0.7871 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0312 | Training Loss 0.4534 | Training F1 0.8024 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0312 | Training Loss 0.4519 | Training F1 0.8026 | Validation Accuracy 0.7814 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0312 | Training Loss 0.4499 | Training F1 0.8044 | Validation Accuracy 0.7795 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0312 | Training Loss 0.4485 | Training F1 0.8038 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0312 | Training Loss 0.4472 | Training F1 0.8064 | Validation Accuracy 0.7909 | Validation F1 0.8116\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0312 | Training Loss 0.4461 | Training F1 0.8056 | Validation Accuracy 0.7909 | Validation F1 0.8097\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0312 | Training Loss 0.4448 | Training F1 0.8056 | Validation Accuracy 0.7890 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0311 | Training Loss 0.4436 | Training F1 0.8035 | Validation Accuracy 0.7909 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0311 | Training Loss 0.4425 | Training F1 0.8059 | Validation Accuracy 0.7928 | Validation F1 0.8111\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0311 | Training Loss 0.4412 | Training F1 0.8068 | Validation Accuracy 0.7871 | Validation F1 0.8049\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0311 | Training Loss 0.4402 | Training F1 0.8066 | Validation Accuracy 0.7928 | Validation F1 0.8117\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0311 | Training Loss 0.4392 | Training F1 0.8068 | Validation Accuracy 0.7909 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0311 | Training Loss 0.4381 | Training F1 0.8037 | Validation Accuracy 0.7985 | Validation F1 0.8140\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0311 | Training Loss 0.4371 | Training F1 0.8059 | Validation Accuracy 0.8023 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0311 | Training Loss 0.4360 | Training F1 0.8068 | Validation Accuracy 0.8023 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0311 | Training Loss 0.4351 | Training F1 0.8047 | Validation Accuracy 0.8023 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0311 | Training Loss 0.4342 | Training F1 0.8099 | Validation Accuracy 0.8042 | Validation F1 0.8209\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0311 | Training Loss 0.4332 | Training F1 0.8053 | Validation Accuracy 0.8004 | Validation F1 0.8155\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0311 | Training Loss 0.4323 | Training F1 0.8094 | Validation Accuracy 0.8061 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0311 | Training Loss 0.4313 | Training F1 0.8102 | Validation Accuracy 0.8042 | Validation F1 0.8196\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0311 | Training Loss 0.4303 | Training F1 0.8107 | Validation Accuracy 0.8042 | Validation F1 0.8196\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0311 | Training Loss 0.4294 | Training F1 0.8117 | Validation Accuracy 0.8061 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0311 | Training Loss 0.4285 | Training F1 0.8151 | Validation Accuracy 0.8042 | Validation F1 0.8196\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0311 | Training Loss 0.4277 | Training F1 0.8137 | Validation Accuracy 0.8080 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0311 | Training Loss 0.4268 | Training F1 0.8160 | Validation Accuracy 0.8042 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0311 | Training Loss 0.4259 | Training F1 0.8162 | Validation Accuracy 0.8080 | Validation F1 0.8231\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0310 | Training Loss 0.4250 | Training F1 0.8171 | Validation Accuracy 0.8042 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0310 | Training Loss 0.4241 | Training F1 0.8176 | Validation Accuracy 0.8042 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0310 | Training Loss 0.4233 | Training F1 0.8172 | Validation Accuracy 0.8118 | Validation F1 0.8266\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0310 | Training Loss 0.4224 | Training F1 0.8156 | Validation Accuracy 0.8099 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0310 | Training Loss 0.4214 | Training F1 0.8156 | Validation Accuracy 0.8099 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0311 | Training Loss 0.4205 | Training F1 0.8163 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0311 | Training Loss 0.4197 | Training F1 0.8161 | Validation Accuracy 0.8080 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0311 | Training Loss 0.4191 | Training F1 0.8174 | Validation Accuracy 0.8061 | Validation F1 0.8198\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0311 | Training Loss 0.4187 | Training F1 0.8203 | Validation Accuracy 0.8042 | Validation F1 0.8233\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0311 | Training Loss 0.4183 | Training F1 0.8206 | Validation Accuracy 0.8061 | Validation F1 0.8185\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0311 | Training Loss 0.4171 | Training F1 0.8168 | Validation Accuracy 0.8023 | Validation F1 0.8194\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0310 | Training Loss 0.4155 | Training F1 0.8173 | Validation Accuracy 0.8042 | Validation F1 0.8209\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0310 | Training Loss 0.4143 | Training F1 0.8188 | Validation Accuracy 0.8099 | Validation F1 0.8227\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0310 | Training Loss 0.4138 | Training F1 0.8235 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0310 | Training Loss 0.4133 | Training F1 0.8195 | Validation Accuracy 0.8004 | Validation F1 0.8122\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0310 | Training Loss 0.4128 | Training F1 0.8233 | Validation Accuracy 0.8061 | Validation F1 0.8247\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0310 | Training Loss 0.4113 | Training F1 0.8258 | Validation Accuracy 0.8137 | Validation F1 0.8275\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0310 | Training Loss 0.4101 | Training F1 0.8238 | Validation Accuracy 0.8137 | Validation F1 0.8275\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa002a35f20>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0419 | Training Loss 3.4526 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0366 | Training Loss 28.2788 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0352 | Training Loss 12.0570 | Training F1 0.7056 | Validation Accuracy 0.5551 | Validation F1 0.7082\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0343 | Training Loss 1.9385 | Training F1 0.7023 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0336 | Training Loss 3.1381 | Training F1 0.7198 | Validation Accuracy 0.6065 | Validation F1 0.7308\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0333 | Training Loss 1.3886 | Training F1 0.4736 | Validation Accuracy 0.5570 | Validation F1 0.4492\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0330 | Training Loss 1.0358 | Training F1 0.2314 | Validation Accuracy 0.4962 | Validation F1 0.1846\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0327 | Training Loss 1.3491 | Training F1 0.5440 | Validation Accuracy 0.6008 | Validation F1 0.5116\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0326 | Training Loss 0.8131 | Training F1 0.7358 | Validation Accuracy 0.6787 | Validation F1 0.7511\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0324 | Training Loss 0.6991 | Training F1 0.7337 | Validation Accuracy 0.6407 | Validation F1 0.7449\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0323 | Training Loss 0.8262 | Training F1 0.7325 | Validation Accuracy 0.6673 | Validation F1 0.7546\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0321 | Training Loss 0.7011 | Training F1 0.7293 | Validation Accuracy 0.6749 | Validation F1 0.7174\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0320 | Training Loss 0.6010 | Training F1 0.6886 | Validation Accuracy 0.6673 | Validation F1 0.6717\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0320 | Training Loss 0.5903 | Training F1 0.6527 | Validation Accuracy 0.6768 | Validation F1 0.6600\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0319 | Training Loss 0.6027 | Training F1 0.6389 | Validation Accuracy 0.6844 | Validation F1 0.6640\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0320 | Training Loss 0.6036 | Training F1 0.6709 | Validation Accuracy 0.6996 | Validation F1 0.6914\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0319 | Training Loss 0.5883 | Training F1 0.7187 | Validation Accuracy 0.7186 | Validation F1 0.7319\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0319 | Training Loss 0.5694 | Training F1 0.7309 | Validation Accuracy 0.7300 | Validation F1 0.7517\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0318 | Training Loss 0.5578 | Training F1 0.7543 | Validation Accuracy 0.7338 | Validation F1 0.7756\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0318 | Training Loss 0.5581 | Training F1 0.7512 | Validation Accuracy 0.7471 | Validation F1 0.7809\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0318 | Training Loss 0.5480 | Training F1 0.7139 | Validation Accuracy 0.7319 | Validation F1 0.7355\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0317 | Training Loss 0.5648 | Training F1 0.7630 | Validation Accuracy 0.7376 | Validation F1 0.7810\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0317 | Training Loss 0.5461 | Training F1 0.7640 | Validation Accuracy 0.7281 | Validation F1 0.7843\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0316 | Training Loss 0.5633 | Training F1 0.7672 | Validation Accuracy 0.7471 | Validation F1 0.7886\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0316 | Training Loss 0.5387 | Training F1 0.7551 | Validation Accuracy 0.7605 | Validation F1 0.7782\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0316 | Training Loss 0.5349 | Training F1 0.7343 | Validation Accuracy 0.7586 | Validation F1 0.7687\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0316 | Training Loss 0.5436 | Training F1 0.7608 | Validation Accuracy 0.7681 | Validation F1 0.7918\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0315 | Training Loss 0.5230 | Training F1 0.7724 | Validation Accuracy 0.7605 | Validation F1 0.7994\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0315 | Training Loss 0.5260 | Training F1 0.7729 | Validation Accuracy 0.7548 | Validation F1 0.7969\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0315 | Training Loss 0.5261 | Training F1 0.7737 | Validation Accuracy 0.7795 | Validation F1 0.8086\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0315 | Training Loss 0.5105 | Training F1 0.7621 | Validation Accuracy 0.7814 | Validation F1 0.7965\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0315 | Training Loss 0.5128 | Training F1 0.7597 | Validation Accuracy 0.7757 | Validation F1 0.7893\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0315 | Training Loss 0.5119 | Training F1 0.7796 | Validation Accuracy 0.7795 | Validation F1 0.8041\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0315 | Training Loss 0.4997 | Training F1 0.7808 | Validation Accuracy 0.7738 | Validation F1 0.8071\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0314 | Training Loss 0.5028 | Training F1 0.7848 | Validation Accuracy 0.7738 | Validation F1 0.8065\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0314 | Training Loss 0.4988 | Training F1 0.7888 | Validation Accuracy 0.7795 | Validation F1 0.8041\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0314 | Training Loss 0.4908 | Training F1 0.7738 | Validation Accuracy 0.7947 | Validation F1 0.8092\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0314 | Training Loss 0.4933 | Training F1 0.7809 | Validation Accuracy 0.7909 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0314 | Training Loss 0.4875 | Training F1 0.7971 | Validation Accuracy 0.7814 | Validation F1 0.8074\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0314 | Training Loss 0.4821 | Training F1 0.7982 | Validation Accuracy 0.7947 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0313 | Training Loss 0.4820 | Training F1 0.7995 | Validation Accuracy 0.7890 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0313 | Training Loss 0.4751 | Training F1 0.7929 | Validation Accuracy 0.7871 | Validation F1 0.8056\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0313 | Training Loss 0.4732 | Training F1 0.7941 | Validation Accuracy 0.7928 | Validation F1 0.8111\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0313 | Training Loss 0.4698 | Training F1 0.8027 | Validation Accuracy 0.7985 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0313 | Training Loss 0.4641 | Training F1 0.8064 | Validation Accuracy 0.7890 | Validation F1 0.8171\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0313 | Training Loss 0.4625 | Training F1 0.8024 | Validation Accuracy 0.8061 | Validation F1 0.8283\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0314 | Training Loss 0.4576 | Training F1 0.7956 | Validation Accuracy 0.8118 | Validation F1 0.8284\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0314 | Training Loss 0.4575 | Training F1 0.8038 | Validation Accuracy 0.8023 | Validation F1 0.8267\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0314 | Training Loss 0.4523 | Training F1 0.8101 | Validation Accuracy 0.7985 | Validation F1 0.8262\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0314 | Training Loss 0.4516 | Training F1 0.8069 | Validation Accuracy 0.8099 | Validation F1 0.8288\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0314 | Training Loss 0.4464 | Training F1 0.8000 | Validation Accuracy 0.8137 | Validation F1 0.8304\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0314 | Training Loss 0.4460 | Training F1 0.8120 | Validation Accuracy 0.8080 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0313 | Training Loss 0.4418 | Training F1 0.8130 | Validation Accuracy 0.8023 | Validation F1 0.8278\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0313 | Training Loss 0.4403 | Training F1 0.8117 | Validation Accuracy 0.8118 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0313 | Training Loss 0.4367 | Training F1 0.8092 | Validation Accuracy 0.8194 | Validation F1 0.8365\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0313 | Training Loss 0.4353 | Training F1 0.8166 | Validation Accuracy 0.8004 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0313 | Training Loss 0.4330 | Training F1 0.8187 | Validation Accuracy 0.8023 | Validation F1 0.8272\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0313 | Training Loss 0.4306 | Training F1 0.8124 | Validation Accuracy 0.8137 | Validation F1 0.8316\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0313 | Training Loss 0.4292 | Training F1 0.8193 | Validation Accuracy 0.8080 | Validation F1 0.8303\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0313 | Training Loss 0.4259 | Training F1 0.8211 | Validation Accuracy 0.8061 | Validation F1 0.8306\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0313 | Training Loss 0.4249 | Training F1 0.8231 | Validation Accuracy 0.8099 | Validation F1 0.8305\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0313 | Training Loss 0.4217 | Training F1 0.8191 | Validation Accuracy 0.8194 | Validation F1 0.8359\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0313 | Training Loss 0.4205 | Training F1 0.8243 | Validation Accuracy 0.8156 | Validation F1 0.8386\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0313 | Training Loss 0.4190 | Training F1 0.8279 | Validation Accuracy 0.8080 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0312 | Training Loss 0.4162 | Training F1 0.8249 | Validation Accuracy 0.8156 | Validation F1 0.8336\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0312 | Training Loss 0.4160 | Training F1 0.8300 | Validation Accuracy 0.8118 | Validation F1 0.8358\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0312 | Training Loss 0.4140 | Training F1 0.8310 | Validation Accuracy 0.8061 | Validation F1 0.8283\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0312 | Training Loss 0.4112 | Training F1 0.8276 | Validation Accuracy 0.8213 | Validation F1 0.8379\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0312 | Training Loss 0.4109 | Training F1 0.8318 | Validation Accuracy 0.8213 | Validation F1 0.8439\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0312 | Training Loss 0.4098 | Training F1 0.8303 | Validation Accuracy 0.8213 | Validation F1 0.8401\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0312 | Training Loss 0.4065 | Training F1 0.8328 | Validation Accuracy 0.8213 | Validation F1 0.8390\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0312 | Training Loss 0.4059 | Training F1 0.8340 | Validation Accuracy 0.8232 | Validation F1 0.8463\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0312 | Training Loss 0.4056 | Training F1 0.8357 | Validation Accuracy 0.8194 | Validation F1 0.8382\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0312 | Training Loss 0.4025 | Training F1 0.8363 | Validation Accuracy 0.8251 | Validation F1 0.8430\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0312 | Training Loss 0.4011 | Training F1 0.8314 | Validation Accuracy 0.8232 | Validation F1 0.8453\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0312 | Training Loss 0.4008 | Training F1 0.8348 | Validation Accuracy 0.8232 | Validation F1 0.8416\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0312 | Training Loss 0.3995 | Training F1 0.8350 | Validation Accuracy 0.8194 | Validation F1 0.8409\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0312 | Training Loss 0.3976 | Training F1 0.8373 | Validation Accuracy 0.8194 | Validation F1 0.8398\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0313 | Training Loss 0.3955 | Training F1 0.8382 | Validation Accuracy 0.8251 | Validation F1 0.8441\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0313 | Training Loss 0.3947 | Training F1 0.8406 | Validation Accuracy 0.8194 | Validation F1 0.8409\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0313 | Training Loss 0.3930 | Training F1 0.8385 | Validation Accuracy 0.8251 | Validation F1 0.8446\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0313 | Training Loss 0.3921 | Training F1 0.8422 | Validation Accuracy 0.8175 | Validation F1 0.8389\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0313 | Training Loss 0.3909 | Training F1 0.8395 | Validation Accuracy 0.8270 | Validation F1 0.8460\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0313 | Training Loss 0.3894 | Training F1 0.8405 | Validation Accuracy 0.8213 | Validation F1 0.8418\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0313 | Training Loss 0.3886 | Training F1 0.8401 | Validation Accuracy 0.8251 | Validation F1 0.8446\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0312 | Training Loss 0.3871 | Training F1 0.8421 | Validation Accuracy 0.8232 | Validation F1 0.8432\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0312 | Training Loss 0.3861 | Training F1 0.8370 | Validation Accuracy 0.8270 | Validation F1 0.8444\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0312 | Training Loss 0.3861 | Training F1 0.8436 | Validation Accuracy 0.8118 | Validation F1 0.8374\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0312 | Training Loss 0.3888 | Training F1 0.8295 | Validation Accuracy 0.8213 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0312 | Training Loss 0.3915 | Training F1 0.8425 | Validation Accuracy 0.8061 | Validation F1 0.8355\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0312 | Training Loss 0.3928 | Training F1 0.8389 | Validation Accuracy 0.8289 | Validation F1 0.8443\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0312 | Training Loss 0.3833 | Training F1 0.8379 | Validation Accuracy 0.8327 | Validation F1 0.8488\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0312 | Training Loss 0.3815 | Training F1 0.8454 | Validation Accuracy 0.8118 | Validation F1 0.8390\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0312 | Training Loss 0.3856 | Training F1 0.8344 | Validation Accuracy 0.8232 | Validation F1 0.8388\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0312 | Training Loss 0.3820 | Training F1 0.8402 | Validation Accuracy 0.8270 | Validation F1 0.8460\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0312 | Training Loss 0.3774 | Training F1 0.8472 | Validation Accuracy 0.8061 | Validation F1 0.8322\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0313 | Training Loss 0.3783 | Training F1 0.8393 | Validation Accuracy 0.8270 | Validation F1 0.8417\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0313 | Training Loss 0.3791 | Training F1 0.8485 | Validation Accuracy 0.8061 | Validation F1 0.8322\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0313 | Training Loss 0.3764 | Training F1 0.8432 | Validation Accuracy 0.8289 | Validation F1 0.8469\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0313 | Training Loss 0.3737 | Training F1 0.8441 | Validation Accuracy 0.8327 | Validation F1 0.8503\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0312 | Training Loss 0.3727 | Training F1 0.8447 | Validation Accuracy 0.8156 | Validation F1 0.8375\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0312 | Training Loss 0.3717 | Training F1 0.8475 | Validation Accuracy 0.8270 | Validation F1 0.8460\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0312 | Training Loss 0.3705 | Training F1 0.8493 | Validation Accuracy 0.8118 | Validation F1 0.8347\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0312 | Training Loss 0.3693 | Training F1 0.8520 | Validation Accuracy 0.8118 | Validation F1 0.8347\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0312 | Training Loss 0.3682 | Training F1 0.8536 | Validation Accuracy 0.8194 | Validation F1 0.8398\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0312 | Training Loss 0.3671 | Training F1 0.8538 | Validation Accuracy 0.8213 | Validation F1 0.8418\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0312 | Training Loss 0.3657 | Training F1 0.8510 | Validation Accuracy 0.8251 | Validation F1 0.8446\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0312 | Training Loss 0.3648 | Training F1 0.8576 | Validation Accuracy 0.8137 | Validation F1 0.8367\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0312 | Training Loss 0.3639 | Training F1 0.8408 | Validation Accuracy 0.8251 | Validation F1 0.8392\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0312 | Training Loss 0.3681 | Training F1 0.8432 | Validation Accuracy 0.7871 | Validation F1 0.8255\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0312 | Training Loss 0.4009 | Training F1 0.8078 | Validation Accuracy 0.8023 | Validation F1 0.8023\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0312 | Training Loss 0.4112 | Training F1 0.8510 | Validation Accuracy 0.8042 | Validation F1 0.8357\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0312 | Training Loss 0.3839 | Training F1 0.8542 | Validation Accuracy 0.8099 | Validation F1 0.8377\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0312 | Training Loss 0.3732 | Training F1 0.8168 | Validation Accuracy 0.8137 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0312 | Training Loss 0.3928 | Training F1 0.8558 | Validation Accuracy 0.8156 | Validation F1 0.8391\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0312 | Training Loss 0.3625 | Training F1 0.8536 | Validation Accuracy 0.8042 | Validation F1 0.8352\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0312 | Training Loss 0.3767 | Training F1 0.8423 | Validation Accuracy 0.8232 | Validation F1 0.8366\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0312 | Training Loss 0.3635 | Training F1 0.8394 | Validation Accuracy 0.8232 | Validation F1 0.8360\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0312 | Training Loss 0.3675 | Training F1 0.8594 | Validation Accuracy 0.8080 | Validation F1 0.8352\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa002a35f20>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0421 | Training Loss 5.7045 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0366 | Training Loss 6.7719 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0348 | Training Loss 2.2515 | Training F1 0.7027 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0339 | Training Loss 1.5941 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0332 | Training Loss 1.9184 | Training F1 0.7023 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0330 | Training Loss 1.2732 | Training F1 0.6965 | Validation Accuracy 0.5798 | Validation F1 0.6993\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0327 | Training Loss 0.6987 | Training F1 0.0087 | Validation Accuracy 0.4582 | Validation F1 0.0070\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0324 | Training Loss 0.9648 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0322 | Training Loss 1.1122 | Training F1 0.0053 | Validation Accuracy 0.4582 | Validation F1 0.0070\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0321 | Training Loss 0.9225 | Training F1 0.3149 | Validation Accuracy 0.5418 | Validation F1 0.3641\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0319 | Training Loss 0.7152 | Training F1 0.6935 | Validation Accuracy 0.6065 | Validation F1 0.7072\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0318 | Training Loss 0.6676 | Training F1 0.7065 | Validation Accuracy 0.5665 | Validation F1 0.7114\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0317 | Training Loss 0.7248 | Training F1 0.7065 | Validation Accuracy 0.5684 | Validation F1 0.7130\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0316 | Training Loss 0.7354 | Training F1 0.7056 | Validation Accuracy 0.5875 | Validation F1 0.7185\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0316 | Training Loss 0.6950 | Training F1 0.6940 | Validation Accuracy 0.5932 | Validation F1 0.6890\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0315 | Training Loss 0.6618 | Training F1 0.5277 | Validation Accuracy 0.6084 | Validation F1 0.5863\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0315 | Training Loss 0.6748 | Training F1 0.5111 | Validation Accuracy 0.5951 | Validation F1 0.5535\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0314 | Training Loss 0.6753 | Training F1 0.6206 | Validation Accuracy 0.6236 | Validation F1 0.6413\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0314 | Training Loss 0.6579 | Training F1 0.6932 | Validation Accuracy 0.6274 | Validation F1 0.7021\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0315 | Training Loss 0.6471 | Training F1 0.7185 | Validation Accuracy 0.6122 | Validation F1 0.7143\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0315 | Training Loss 0.6492 | Training F1 0.7203 | Validation Accuracy 0.6141 | Validation F1 0.7231\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0314 | Training Loss 0.6539 | Training F1 0.7205 | Validation Accuracy 0.6141 | Validation F1 0.7231\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0314 | Training Loss 0.6528 | Training F1 0.7246 | Validation Accuracy 0.6236 | Validation F1 0.7250\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0313 | Training Loss 0.6451 | Training F1 0.7188 | Validation Accuracy 0.6388 | Validation F1 0.7254\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0313 | Training Loss 0.6359 | Training F1 0.6951 | Validation Accuracy 0.6464 | Validation F1 0.7029\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0313 | Training Loss 0.6316 | Training F1 0.6684 | Validation Accuracy 0.6559 | Validation F1 0.6885\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0312 | Training Loss 0.6305 | Training F1 0.6752 | Validation Accuracy 0.6597 | Validation F1 0.6940\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0312 | Training Loss 0.6243 | Training F1 0.7005 | Validation Accuracy 0.6768 | Validation F1 0.7240\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0312 | Training Loss 0.6153 | Training F1 0.7252 | Validation Accuracy 0.6673 | Validation F1 0.7278\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0312 | Training Loss 0.6093 | Training F1 0.7355 | Validation Accuracy 0.6673 | Validation F1 0.7344\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0311 | Training Loss 0.6062 | Training F1 0.7381 | Validation Accuracy 0.6806 | Validation F1 0.7447\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0311 | Training Loss 0.6008 | Training F1 0.7371 | Validation Accuracy 0.6901 | Validation F1 0.7425\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0311 | Training Loss 0.5923 | Training F1 0.7166 | Validation Accuracy 0.7034 | Validation F1 0.7400\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0311 | Training Loss 0.5860 | Training F1 0.7143 | Validation Accuracy 0.7053 | Validation F1 0.7341\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0311 | Training Loss 0.5825 | Training F1 0.7196 | Validation Accuracy 0.7110 | Validation F1 0.7406\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0311 | Training Loss 0.5755 | Training F1 0.7345 | Validation Accuracy 0.7186 | Validation F1 0.7574\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0311 | Training Loss 0.5683 | Training F1 0.7510 | Validation Accuracy 0.7300 | Validation F1 0.7739\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0311 | Training Loss 0.5652 | Training F1 0.7468 | Validation Accuracy 0.7262 | Validation F1 0.7662\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0311 | Training Loss 0.5574 | Training F1 0.7311 | Validation Accuracy 0.7262 | Validation F1 0.7526\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0310 | Training Loss 0.5533 | Training F1 0.7381 | Validation Accuracy 0.7338 | Validation F1 0.7619\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0310 | Training Loss 0.5468 | Training F1 0.7617 | Validation Accuracy 0.7452 | Validation F1 0.7825\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0310 | Training Loss 0.5407 | Training F1 0.7653 | Validation Accuracy 0.7510 | Validation F1 0.7890\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0310 | Training Loss 0.5356 | Training F1 0.7551 | Validation Accuracy 0.7414 | Validation F1 0.7679\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0310 | Training Loss 0.5327 | Training F1 0.7652 | Validation Accuracy 0.7605 | Validation F1 0.7934\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0310 | Training Loss 0.5258 | Training F1 0.7763 | Validation Accuracy 0.7700 | Validation F1 0.8064\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0310 | Training Loss 0.5241 | Training F1 0.7532 | Validation Accuracy 0.7567 | Validation F1 0.7762\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0310 | Training Loss 0.5238 | Training F1 0.7751 | Validation Accuracy 0.7643 | Validation F1 0.7994\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0310 | Training Loss 0.5191 | Training F1 0.7653 | Validation Accuracy 0.7776 | Validation F1 0.8020\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0310 | Training Loss 0.5130 | Training F1 0.7595 | Validation Accuracy 0.7757 | Validation F1 0.7973\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0310 | Training Loss 0.5123 | Training F1 0.7806 | Validation Accuracy 0.7681 | Validation F1 0.8013\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0310 | Training Loss 0.5121 | Training F1 0.7700 | Validation Accuracy 0.7776 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0310 | Training Loss 0.5056 | Training F1 0.7739 | Validation Accuracy 0.7776 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0310 | Training Loss 0.5029 | Training F1 0.7837 | Validation Accuracy 0.7795 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0310 | Training Loss 0.5030 | Training F1 0.7758 | Validation Accuracy 0.7776 | Validation F1 0.8027\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0310 | Training Loss 0.4972 | Training F1 0.7750 | Validation Accuracy 0.7700 | Validation F1 0.7925\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0310 | Training Loss 0.4954 | Training F1 0.7897 | Validation Accuracy 0.7928 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0310 | Training Loss 0.4942 | Training F1 0.7815 | Validation Accuracy 0.7947 | Validation F1 0.8188\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0310 | Training Loss 0.4892 | Training F1 0.7774 | Validation Accuracy 0.7719 | Validation F1 0.7917\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0310 | Training Loss 0.4878 | Training F1 0.7909 | Validation Accuracy 0.7852 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0310 | Training Loss 0.4873 | Training F1 0.7780 | Validation Accuracy 0.7605 | Validation F1 0.7766\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0310 | Training Loss 0.4835 | Training F1 0.7913 | Validation Accuracy 0.7852 | Validation F1 0.8138\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0310 | Training Loss 0.4782 | Training F1 0.7928 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0310 | Training Loss 0.4749 | Training F1 0.7847 | Validation Accuracy 0.7890 | Validation F1 0.8049\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0310 | Training Loss 0.4751 | Training F1 0.7972 | Validation Accuracy 0.7852 | Validation F1 0.8151\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0309 | Training Loss 0.4752 | Training F1 0.7850 | Validation Accuracy 0.7909 | Validation F1 0.8050\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0309 | Training Loss 0.4714 | Training F1 0.7978 | Validation Accuracy 0.7700 | Validation F1 0.7980\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0309 | Training Loss 0.4665 | Training F1 0.7988 | Validation Accuracy 0.7776 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0309 | Training Loss 0.4627 | Training F1 0.7961 | Validation Accuracy 0.7890 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0309 | Training Loss 0.4617 | Training F1 0.8025 | Validation Accuracy 0.7757 | Validation F1 0.8072\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0309 | Training Loss 0.4624 | Training F1 0.7923 | Validation Accuracy 0.7947 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0309 | Training Loss 0.4634 | Training F1 0.8051 | Validation Accuracy 0.7757 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0309 | Training Loss 0.4644 | Training F1 0.7916 | Validation Accuracy 0.8004 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0309 | Training Loss 0.4584 | Training F1 0.8063 | Validation Accuracy 0.7833 | Validation F1 0.8087\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0309 | Training Loss 0.4522 | Training F1 0.8060 | Validation Accuracy 0.7909 | Validation F1 0.8142\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0309 | Training Loss 0.4494 | Training F1 0.7955 | Validation Accuracy 0.7966 | Validation F1 0.8120\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0309 | Training Loss 0.4505 | Training F1 0.8083 | Validation Accuracy 0.7833 | Validation F1 0.8131\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0309 | Training Loss 0.4522 | Training F1 0.7933 | Validation Accuracy 0.8099 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0309 | Training Loss 0.4491 | Training F1 0.8094 | Validation Accuracy 0.7909 | Validation F1 0.8154\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0310 | Training Loss 0.4445 | Training F1 0.8068 | Validation Accuracy 0.7985 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0310 | Training Loss 0.4418 | Training F1 0.8039 | Validation Accuracy 0.8099 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0310 | Training Loss 0.4417 | Training F1 0.8104 | Validation Accuracy 0.7890 | Validation F1 0.8189\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0310 | Training Loss 0.4432 | Training F1 0.8049 | Validation Accuracy 0.8023 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0310 | Training Loss 0.4426 | Training F1 0.8134 | Validation Accuracy 0.7871 | Validation F1 0.8164\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0310 | Training Loss 0.4404 | Training F1 0.8101 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0310 | Training Loss 0.4359 | Training F1 0.8123 | Validation Accuracy 0.8061 | Validation F1 0.8253\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0310 | Training Loss 0.4334 | Training F1 0.8148 | Validation Accuracy 0.7985 | Validation F1 0.8227\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0310 | Training Loss 0.4335 | Training F1 0.8090 | Validation Accuracy 0.8099 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0310 | Training Loss 0.4341 | Training F1 0.8135 | Validation Accuracy 0.7966 | Validation F1 0.8249\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0310 | Training Loss 0.4362 | Training F1 0.7991 | Validation Accuracy 0.8042 | Validation F1 0.8117\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0310 | Training Loss 0.4386 | Training F1 0.8154 | Validation Accuracy 0.7814 | Validation F1 0.8160\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0310 | Training Loss 0.4435 | Training F1 0.7940 | Validation Accuracy 0.8061 | Validation F1 0.8139\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0310 | Training Loss 0.4386 | Training F1 0.8182 | Validation Accuracy 0.7890 | Validation F1 0.8189\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0310 | Training Loss 0.4313 | Training F1 0.8191 | Validation Accuracy 0.7985 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0310 | Training Loss 0.4236 | Training F1 0.8100 | Validation Accuracy 0.8004 | Validation F1 0.8142\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0310 | Training Loss 0.4268 | Training F1 0.8185 | Validation Accuracy 0.7852 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0310 | Training Loss 0.4326 | Training F1 0.8108 | Validation Accuracy 0.7985 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0310 | Training Loss 0.4263 | Training F1 0.8217 | Validation Accuracy 0.7985 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0310 | Training Loss 0.4193 | Training F1 0.8209 | Validation Accuracy 0.7985 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0310 | Training Loss 0.4188 | Training F1 0.8104 | Validation Accuracy 0.7966 | Validation F1 0.8079\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0310 | Training Loss 0.4229 | Training F1 0.8227 | Validation Accuracy 0.7833 | Validation F1 0.8155\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0310 | Training Loss 0.4276 | Training F1 0.8033 | Validation Accuracy 0.8042 | Validation F1 0.8131\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0310 | Training Loss 0.4237 | Training F1 0.8238 | Validation Accuracy 0.7814 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0310 | Training Loss 0.4179 | Training F1 0.8227 | Validation Accuracy 0.8023 | Validation F1 0.8213\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0310 | Training Loss 0.4132 | Training F1 0.8158 | Validation Accuracy 0.7985 | Validation F1 0.8134\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0310 | Training Loss 0.4142 | Training F1 0.8256 | Validation Accuracy 0.7795 | Validation F1 0.8098\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0310 | Training Loss 0.4172 | Training F1 0.8115 | Validation Accuracy 0.7947 | Validation F1 0.8092\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0310 | Training Loss 0.4152 | Training F1 0.8247 | Validation Accuracy 0.7890 | Validation F1 0.8159\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0310 | Training Loss 0.4113 | Training F1 0.8242 | Validation Accuracy 0.7985 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0310 | Training Loss 0.4083 | Training F1 0.8220 | Validation Accuracy 0.8023 | Validation F1 0.8207\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0310 | Training Loss 0.4076 | Training F1 0.8275 | Validation Accuracy 0.7890 | Validation F1 0.8159\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0310 | Training Loss 0.4088 | Training F1 0.8130 | Validation Accuracy 0.7985 | Validation F1 0.8114\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0310 | Training Loss 0.4116 | Training F1 0.8281 | Validation Accuracy 0.7909 | Validation F1 0.8203\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0310 | Training Loss 0.4151 | Training F1 0.8082 | Validation Accuracy 0.8042 | Validation F1 0.8151\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0310 | Training Loss 0.4151 | Training F1 0.8300 | Validation Accuracy 0.7928 | Validation F1 0.8222\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0311 | Training Loss 0.4122 | Training F1 0.8219 | Validation Accuracy 0.7947 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0311 | Training Loss 0.4059 | Training F1 0.8276 | Validation Accuracy 0.7890 | Validation F1 0.8122\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0311 | Training Loss 0.4014 | Training F1 0.8264 | Validation Accuracy 0.7909 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0311 | Training Loss 0.4007 | Training F1 0.8231 | Validation Accuracy 0.7966 | Validation F1 0.8120\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0311 | Training Loss 0.4016 | Training F1 0.8360 | Validation Accuracy 0.7947 | Validation F1 0.8235\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7fa002a35820>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0427 | Training Loss 11.0198 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0375 | Training Loss 16.7320 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0358 | Training Loss 8.0826 | Training F1 0.6467 | Validation Accuracy 0.5570 | Validation F1 0.6307\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0367 | Training Loss 0.7366 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0356 | Training Loss 3.7851 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0349 | Training Loss 3.4446 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0344 | Training Loss 1.4828 | Training F1 0.0035 | Validation Accuracy 0.4468 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0351 | Training Loss 1.7767 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0347 | Training Loss 1.9172 | Training F1 0.0017 | Validation Accuracy 0.4506 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0348 | Training Loss 1.1146 | Training F1 0.6716 | Validation Accuracy 0.5856 | Validation F1 0.6766\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0369 | Training Loss 0.6675 | Training F1 0.7041 | Validation Accuracy 0.5475 | Validation F1 0.7054\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0373 | Training Loss 0.8123 | Training F1 0.7027 | Validation Accuracy 0.5475 | Validation F1 0.7054\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0370 | Training Loss 0.8439 | Training F1 0.7028 | Validation Accuracy 0.5494 | Validation F1 0.7063\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0366 | Training Loss 0.7584 | Training F1 0.7055 | Validation Accuracy 0.5646 | Validation F1 0.7075\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0362 | Training Loss 0.6833 | Training F1 0.6241 | Validation Accuracy 0.5875 | Validation F1 0.6291\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0358 | Training Loss 0.6791 | Training F1 0.1396 | Validation Accuracy 0.4468 | Validation F1 0.1101\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0355 | Training Loss 0.7077 | Training F1 0.0403 | Validation Accuracy 0.4582 | Validation F1 0.0594\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0352 | Training Loss 0.7272 | Training F1 0.0405 | Validation Accuracy 0.4582 | Validation F1 0.0339\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0349 | Training Loss 0.7227 | Training F1 0.1650 | Validation Accuracy 0.4487 | Validation F1 0.1049\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0347 | Training Loss 0.7012 | Training F1 0.4891 | Validation Accuracy 0.5228 | Validation F1 0.4508\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0344 | Training Loss 0.6767 | Training F1 0.6567 | Validation Accuracy 0.6407 | Validation F1 0.6494\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0344 | Training Loss 0.6514 | Training F1 0.3356 | Validation Accuracy 0.5171 | Validation F1 0.3209\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0346 | Training Loss 0.6702 | Training F1 0.7266 | Validation Accuracy 0.6768 | Validation F1 0.7109\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0344 | Training Loss 0.6307 | Training F1 0.7476 | Validation Accuracy 0.6559 | Validation F1 0.7440\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0343 | Training Loss 0.6338 | Training F1 0.7411 | Validation Accuracy 0.6198 | Validation F1 0.7312\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0341 | Training Loss 0.6362 | Training F1 0.7437 | Validation Accuracy 0.6350 | Validation F1 0.7412\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0340 | Training Loss 0.6283 | Training F1 0.7557 | Validation Accuracy 0.6787 | Validation F1 0.7554\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0338 | Training Loss 0.6073 | Training F1 0.7412 | Validation Accuracy 0.7148 | Validation F1 0.7525\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0337 | Training Loss 0.5900 | Training F1 0.6620 | Validation Accuracy 0.6654 | Validation F1 0.6641\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0336 | Training Loss 0.6013 | Training F1 0.7000 | Validation Accuracy 0.7034 | Validation F1 0.7214\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0335 | Training Loss 0.5840 | Training F1 0.7596 | Validation Accuracy 0.7395 | Validation F1 0.7787\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0334 | Training Loss 0.5676 | Training F1 0.7576 | Validation Accuracy 0.7072 | Validation F1 0.7688\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0333 | Training Loss 0.5701 | Training F1 0.7622 | Validation Accuracy 0.7148 | Validation F1 0.7727\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0332 | Training Loss 0.5655 | Training F1 0.7626 | Validation Accuracy 0.7395 | Validation F1 0.7808\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0332 | Training Loss 0.5501 | Training F1 0.7417 | Validation Accuracy 0.7471 | Validation F1 0.7687\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0331 | Training Loss 0.5478 | Training F1 0.7321 | Validation Accuracy 0.7414 | Validation F1 0.7580\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0330 | Training Loss 0.5499 | Training F1 0.7556 | Validation Accuracy 0.7510 | Validation F1 0.7753\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0329 | Training Loss 0.5357 | Training F1 0.7688 | Validation Accuracy 0.7548 | Validation F1 0.7929\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0329 | Training Loss 0.5340 | Training F1 0.7747 | Validation Accuracy 0.7395 | Validation F1 0.7849\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0328 | Training Loss 0.5345 | Training F1 0.7743 | Validation Accuracy 0.7586 | Validation F1 0.7935\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0327 | Training Loss 0.5242 | Training F1 0.7582 | Validation Accuracy 0.7681 | Validation F1 0.7897\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0327 | Training Loss 0.5206 | Training F1 0.7551 | Validation Accuracy 0.7681 | Validation F1 0.7852\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0326 | Training Loss 0.5209 | Training F1 0.7665 | Validation Accuracy 0.7738 | Validation F1 0.7973\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0326 | Training Loss 0.5107 | Training F1 0.7848 | Validation Accuracy 0.7605 | Validation F1 0.7955\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0325 | Training Loss 0.5097 | Training F1 0.7855 | Validation Accuracy 0.7567 | Validation F1 0.7935\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0325 | Training Loss 0.5074 | Training F1 0.7802 | Validation Accuracy 0.7871 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0324 | Training Loss 0.4995 | Training F1 0.7718 | Validation Accuracy 0.7871 | Validation F1 0.8069\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0324 | Training Loss 0.5004 | Training F1 0.7762 | Validation Accuracy 0.7852 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0323 | Training Loss 0.4957 | Training F1 0.7861 | Validation Accuracy 0.7871 | Validation F1 0.8146\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0323 | Training Loss 0.4920 | Training F1 0.7844 | Validation Accuracy 0.7795 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0323 | Training Loss 0.4919 | Training F1 0.7847 | Validation Accuracy 0.7852 | Validation F1 0.8132\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0322 | Training Loss 0.4868 | Training F1 0.7812 | Validation Accuracy 0.7852 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0322 | Training Loss 0.4862 | Training F1 0.7844 | Validation Accuracy 0.7890 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0322 | Training Loss 0.4835 | Training F1 0.7891 | Validation Accuracy 0.7928 | Validation F1 0.8216\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0322 | Training Loss 0.4801 | Training F1 0.7899 | Validation Accuracy 0.7852 | Validation F1 0.8180\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0322 | Training Loss 0.4788 | Training F1 0.7906 | Validation Accuracy 0.7909 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0321 | Training Loss 0.4747 | Training F1 0.7825 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0321 | Training Loss 0.4740 | Training F1 0.7891 | Validation Accuracy 0.7928 | Validation F1 0.8180\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0321 | Training Loss 0.4698 | Training F1 0.7962 | Validation Accuracy 0.7814 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0320 | Training Loss 0.4687 | Training F1 0.7907 | Validation Accuracy 0.7871 | Validation F1 0.8164\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0320 | Training Loss 0.4650 | Training F1 0.7920 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0320 | Training Loss 0.4639 | Training F1 0.7947 | Validation Accuracy 0.7852 | Validation F1 0.8120\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0320 | Training Loss 0.4610 | Training F1 0.7972 | Validation Accuracy 0.7795 | Validation F1 0.8123\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0319 | Training Loss 0.4601 | Training F1 0.7952 | Validation Accuracy 0.7928 | Validation F1 0.8192\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0319 | Training Loss 0.4576 | Training F1 0.7993 | Validation Accuracy 0.8061 | Validation F1 0.8265\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0319 | Training Loss 0.4566 | Training F1 0.7992 | Validation Accuracy 0.7871 | Validation F1 0.8176\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0319 | Training Loss 0.4542 | Training F1 0.8013 | Validation Accuracy 0.7871 | Validation F1 0.8164\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0318 | Training Loss 0.4521 | Training F1 0.8009 | Validation Accuracy 0.8042 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0318 | Training Loss 0.4508 | Training F1 0.8052 | Validation Accuracy 0.7947 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0318 | Training Loss 0.4487 | Training F1 0.8061 | Validation Accuracy 0.7928 | Validation F1 0.8192\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0318 | Training Loss 0.4473 | Training F1 0.8041 | Validation Accuracy 0.8061 | Validation F1 0.8271\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0318 | Training Loss 0.4461 | Training F1 0.8056 | Validation Accuracy 0.8004 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0317 | Training Loss 0.4440 | Training F1 0.8078 | Validation Accuracy 0.8004 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0317 | Training Loss 0.4416 | Training F1 0.8092 | Validation Accuracy 0.8004 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0317 | Training Loss 0.4404 | Training F1 0.8076 | Validation Accuracy 0.8042 | Validation F1 0.8280\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0317 | Training Loss 0.4384 | Training F1 0.8100 | Validation Accuracy 0.8061 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0317 | Training Loss 0.4366 | Training F1 0.8084 | Validation Accuracy 0.8118 | Validation F1 0.8353\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0316 | Training Loss 0.4353 | Training F1 0.8105 | Validation Accuracy 0.8080 | Validation F1 0.8262\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0316 | Training Loss 0.4328 | Training F1 0.8113 | Validation Accuracy 0.8061 | Validation F1 0.8300\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0324 | Training Loss 0.4316 | Training F1 0.8015 | Validation Accuracy 0.7928 | Validation F1 0.8050\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0324 | Training Loss 0.4386 | Training F1 0.8104 | Validation Accuracy 0.7814 | Validation F1 0.8212\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0324 | Training Loss 0.4534 | Training F1 0.8061 | Validation Accuracy 0.8042 | Validation F1 0.8177\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0324 | Training Loss 0.4339 | Training F1 0.8132 | Validation Accuracy 0.8080 | Validation F1 0.8231\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0324 | Training Loss 0.4299 | Training F1 0.8155 | Validation Accuracy 0.7909 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0324 | Training Loss 0.4385 | Training F1 0.8119 | Validation Accuracy 0.8099 | Validation F1 0.8311\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0324 | Training Loss 0.4269 | Training F1 0.8015 | Validation Accuracy 0.8137 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0323 | Training Loss 0.4365 | Training F1 0.8140 | Validation Accuracy 0.8099 | Validation F1 0.8328\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0324 | Training Loss 0.4244 | Training F1 0.8130 | Validation Accuracy 0.7928 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0324 | Training Loss 0.4288 | Training F1 0.8113 | Validation Accuracy 0.8080 | Validation F1 0.8231\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0324 | Training Loss 0.4229 | Training F1 0.8130 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0323 | Training Loss 0.4218 | Training F1 0.8174 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0323 | Training Loss 0.4227 | Training F1 0.8195 | Validation Accuracy 0.8213 | Validation F1 0.8396\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0323 | Training Loss 0.4157 | Training F1 0.8138 | Validation Accuracy 0.8270 | Validation F1 0.8348\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0323 | Training Loss 0.4201 | Training F1 0.8177 | Validation Accuracy 0.8156 | Validation F1 0.8359\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0322 | Training Loss 0.4156 | Training F1 0.8212 | Validation Accuracy 0.8156 | Validation F1 0.8353\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0322 | Training Loss 0.4125 | Training F1 0.8183 | Validation Accuracy 0.8194 | Validation F1 0.8288\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0322 | Training Loss 0.4146 | Training F1 0.8209 | Validation Accuracy 0.8137 | Validation F1 0.8356\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0322 | Training Loss 0.4105 | Training F1 0.8258 | Validation Accuracy 0.8194 | Validation F1 0.8387\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0322 | Training Loss 0.4079 | Training F1 0.8154 | Validation Accuracy 0.8137 | Validation F1 0.8250\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0321 | Training Loss 0.4101 | Training F1 0.8255 | Validation Accuracy 0.8251 | Validation F1 0.8451\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0321 | Training Loss 0.4062 | Training F1 0.8270 | Validation Accuracy 0.8289 | Validation F1 0.8448\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0321 | Training Loss 0.4025 | Training F1 0.8237 | Validation Accuracy 0.8213 | Validation F1 0.8315\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0321 | Training Loss 0.4048 | Training F1 0.8295 | Validation Accuracy 0.8232 | Validation F1 0.8442\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0321 | Training Loss 0.4047 | Training F1 0.8241 | Validation Accuracy 0.8213 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0321 | Training Loss 0.3996 | Training F1 0.8303 | Validation Accuracy 0.8289 | Validation F1 0.8432\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0320 | Training Loss 0.3976 | Training F1 0.8335 | Validation Accuracy 0.8194 | Validation F1 0.8393\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0320 | Training Loss 0.3988 | Training F1 0.8219 | Validation Accuracy 0.8213 | Validation F1 0.8327\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0320 | Training Loss 0.3983 | Training F1 0.8378 | Validation Accuracy 0.8194 | Validation F1 0.8387\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0320 | Training Loss 0.3960 | Training F1 0.8276 | Validation Accuracy 0.8289 | Validation F1 0.8421\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0320 | Training Loss 0.3927 | Training F1 0.8341 | Validation Accuracy 0.8346 | Validation F1 0.8503\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0320 | Training Loss 0.3904 | Training F1 0.8406 | Validation Accuracy 0.8270 | Validation F1 0.8444\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0319 | Training Loss 0.3894 | Training F1 0.8254 | Validation Accuracy 0.8270 | Validation F1 0.8401\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0319 | Training Loss 0.3895 | Training F1 0.8449 | Validation Accuracy 0.8156 | Validation F1 0.8375\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0319 | Training Loss 0.3901 | Training F1 0.8223 | Validation Accuracy 0.8137 | Validation F1 0.8244\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0319 | Training Loss 0.3925 | Training F1 0.8426 | Validation Accuracy 0.8042 | Validation F1 0.8314\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0320 | Training Loss 0.3964 | Training F1 0.8183 | Validation Accuracy 0.8080 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0320 | Training Loss 0.3990 | Training F1 0.8439 | Validation Accuracy 0.8042 | Validation F1 0.8314\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0320 | Training Loss 0.3926 | Training F1 0.8359 | Validation Accuracy 0.8327 | Validation F1 0.8467\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0320 | Training Loss 0.3829 | Training F1 0.8305 | Validation Accuracy 0.8308 | Validation F1 0.8425\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7f9f68416270>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0424 | Training Loss 9.1163 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0371 | Training Loss 12.1420 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0353 | Training Loss 7.1659 | Training F1 0.5179 | Validation Accuracy 0.4848 | Validation F1 0.5352\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0343 | Training Loss 0.9650 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0337 | Training Loss 3.8729 | Training F1 0.0035 | Validation Accuracy 0.4563 | Validation F1 0.0069\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0334 | Training Loss 1.9168 | Training F1 0.6938 | Validation Accuracy 0.5494 | Validation F1 0.6877\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0330 | Training Loss 0.8030 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0327 | Training Loss 1.4424 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0327 | Training Loss 1.3764 | Training F1 0.7024 | Validation Accuracy 0.5399 | Validation F1 0.7012\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0325 | Training Loss 0.9084 | Training F1 0.5995 | Validation Accuracy 0.5551 | Validation F1 0.5966\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0323 | Training Loss 0.6676 | Training F1 0.1337 | Validation Accuracy 0.4848 | Validation F1 0.1610\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0322 | Training Loss 0.7988 | Training F1 0.2080 | Validation Accuracy 0.5228 | Validation F1 0.2890\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0321 | Training Loss 0.7497 | Training F1 0.6733 | Validation Accuracy 0.6046 | Validation F1 0.6623\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0320 | Training Loss 0.6409 | Training F1 0.7078 | Validation Accuracy 0.5532 | Validation F1 0.7073\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0319 | Training Loss 0.6931 | Training F1 0.7072 | Validation Accuracy 0.5608 | Validation F1 0.7116\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0319 | Training Loss 0.6816 | Training F1 0.7324 | Validation Accuracy 0.6274 | Validation F1 0.7247\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0318 | Training Loss 0.6221 | Training F1 0.5632 | Validation Accuracy 0.6502 | Validation F1 0.6000\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0318 | Training Loss 0.6398 | Training F1 0.4003 | Validation Accuracy 0.5856 | Validation F1 0.4410\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0317 | Training Loss 0.6738 | Training F1 0.5759 | Validation Accuracy 0.6616 | Validation F1 0.6114\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0317 | Training Loss 0.6352 | Training F1 0.7319 | Validation Accuracy 0.6483 | Validation F1 0.7346\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0316 | Training Loss 0.6132 | Training F1 0.7183 | Validation Accuracy 0.5817 | Validation F1 0.7187\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0316 | Training Loss 0.6429 | Training F1 0.7336 | Validation Accuracy 0.6122 | Validation F1 0.7236\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0316 | Training Loss 0.6140 | Training F1 0.7205 | Validation Accuracy 0.6939 | Validation F1 0.7257\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0315 | Training Loss 0.5994 | Training F1 0.6209 | Validation Accuracy 0.6749 | Validation F1 0.6460\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0316 | Training Loss 0.6121 | Training F1 0.6569 | Validation Accuracy 0.6920 | Validation F1 0.6798\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0316 | Training Loss 0.6008 | Training F1 0.7347 | Validation Accuracy 0.6996 | Validation F1 0.7393\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0316 | Training Loss 0.5828 | Training F1 0.7567 | Validation Accuracy 0.6597 | Validation F1 0.7402\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0315 | Training Loss 0.5859 | Training F1 0.7594 | Validation Accuracy 0.6597 | Validation F1 0.7432\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0315 | Training Loss 0.5867 | Training F1 0.7573 | Validation Accuracy 0.6977 | Validation F1 0.7527\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0315 | Training Loss 0.5704 | Training F1 0.7314 | Validation Accuracy 0.7243 | Validation F1 0.7434\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0315 | Training Loss 0.5644 | Training F1 0.7006 | Validation Accuracy 0.7186 | Validation F1 0.7186\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0315 | Training Loss 0.5670 | Training F1 0.7134 | Validation Accuracy 0.7376 | Validation F1 0.7482\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0314 | Training Loss 0.5578 | Training F1 0.7478 | Validation Accuracy 0.7300 | Validation F1 0.7593\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0314 | Training Loss 0.5481 | Training F1 0.7625 | Validation Accuracy 0.7205 | Validation F1 0.7656\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0314 | Training Loss 0.5495 | Training F1 0.7643 | Validation Accuracy 0.7300 | Validation F1 0.7732\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0314 | Training Loss 0.5461 | Training F1 0.7556 | Validation Accuracy 0.7414 | Validation F1 0.7718\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0314 | Training Loss 0.5363 | Training F1 0.7349 | Validation Accuracy 0.7433 | Validation F1 0.7602\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0314 | Training Loss 0.5353 | Training F1 0.7344 | Validation Accuracy 0.7452 | Validation F1 0.7572\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0314 | Training Loss 0.5333 | Training F1 0.7543 | Validation Accuracy 0.7586 | Validation F1 0.7814\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0314 | Training Loss 0.5254 | Training F1 0.7671 | Validation Accuracy 0.7529 | Validation F1 0.7862\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0314 | Training Loss 0.5221 | Training F1 0.7692 | Validation Accuracy 0.7605 | Validation F1 0.7961\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0314 | Training Loss 0.5193 | Training F1 0.7672 | Validation Accuracy 0.7624 | Validation F1 0.7906\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0314 | Training Loss 0.5132 | Training F1 0.7564 | Validation Accuracy 0.7643 | Validation F1 0.7840\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0314 | Training Loss 0.5119 | Training F1 0.7592 | Validation Accuracy 0.7624 | Validation F1 0.7818\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0314 | Training Loss 0.5081 | Training F1 0.7771 | Validation Accuracy 0.7605 | Validation F1 0.7921\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0314 | Training Loss 0.5036 | Training F1 0.7811 | Validation Accuracy 0.7567 | Validation F1 0.7915\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0314 | Training Loss 0.5023 | Training F1 0.7792 | Validation Accuracy 0.7567 | Validation F1 0.7881\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0313 | Training Loss 0.4970 | Training F1 0.7767 | Validation Accuracy 0.7624 | Validation F1 0.7826\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0313 | Training Loss 0.4950 | Training F1 0.7814 | Validation Accuracy 0.7624 | Validation F1 0.7856\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0313 | Training Loss 0.4914 | Training F1 0.7856 | Validation Accuracy 0.7757 | Validation F1 0.8046\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0313 | Training Loss 0.4874 | Training F1 0.7903 | Validation Accuracy 0.7738 | Validation F1 0.8046\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0313 | Training Loss 0.4847 | Training F1 0.7915 | Validation Accuracy 0.7776 | Validation F1 0.8040\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0313 | Training Loss 0.4805 | Training F1 0.7900 | Validation Accuracy 0.7776 | Validation F1 0.7979\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0313 | Training Loss 0.4791 | Training F1 0.7966 | Validation Accuracy 0.7776 | Validation F1 0.8066\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0313 | Training Loss 0.4753 | Training F1 0.7983 | Validation Accuracy 0.7738 | Validation F1 0.8046\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0313 | Training Loss 0.4733 | Training F1 0.7961 | Validation Accuracy 0.7833 | Validation F1 0.8041\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0313 | Training Loss 0.4704 | Training F1 0.8007 | Validation Accuracy 0.7833 | Validation F1 0.8055\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0313 | Training Loss 0.4672 | Training F1 0.8024 | Validation Accuracy 0.7814 | Validation F1 0.8105\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0313 | Training Loss 0.4659 | Training F1 0.7988 | Validation Accuracy 0.7928 | Validation F1 0.8130\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0313 | Training Loss 0.4628 | Training F1 0.8014 | Validation Accuracy 0.7909 | Validation F1 0.8116\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0313 | Training Loss 0.4603 | Training F1 0.8053 | Validation Accuracy 0.7947 | Validation F1 0.8200\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0313 | Training Loss 0.4579 | Training F1 0.8036 | Validation Accuracy 0.7890 | Validation F1 0.8115\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0313 | Training Loss 0.4547 | Training F1 0.8053 | Validation Accuracy 0.7890 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0313 | Training Loss 0.4530 | Training F1 0.8086 | Validation Accuracy 0.7871 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0313 | Training Loss 0.4522 | Training F1 0.8023 | Validation Accuracy 0.7871 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0313 | Training Loss 0.4510 | Training F1 0.8084 | Validation Accuracy 0.7947 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0313 | Training Loss 0.4486 | Training F1 0.8060 | Validation Accuracy 0.7852 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0312 | Training Loss 0.4443 | Training F1 0.8094 | Validation Accuracy 0.7814 | Validation F1 0.8054\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0312 | Training Loss 0.4408 | Training F1 0.8122 | Validation Accuracy 0.7947 | Validation F1 0.8194\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0312 | Training Loss 0.4398 | Training F1 0.8077 | Validation Accuracy 0.7795 | Validation F1 0.7979\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0312 | Training Loss 0.4390 | Training F1 0.8151 | Validation Accuracy 0.7909 | Validation F1 0.8161\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0313 | Training Loss 0.4372 | Training F1 0.8111 | Validation Accuracy 0.7890 | Validation F1 0.8090\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0312 | Training Loss 0.4345 | Training F1 0.8144 | Validation Accuracy 0.7928 | Validation F1 0.8156\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0312 | Training Loss 0.4318 | Training F1 0.8171 | Validation Accuracy 0.7947 | Validation F1 0.8176\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0312 | Training Loss 0.4300 | Training F1 0.8131 | Validation Accuracy 0.7833 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0312 | Training Loss 0.4289 | Training F1 0.8198 | Validation Accuracy 0.7928 | Validation F1 0.8186\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0312 | Training Loss 0.4285 | Training F1 0.8117 | Validation Accuracy 0.7738 | Validation F1 0.7894\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0312 | Training Loss 0.4280 | Training F1 0.8193 | Validation Accuracy 0.7928 | Validation F1 0.8192\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0312 | Training Loss 0.4269 | Training F1 0.8173 | Validation Accuracy 0.7719 | Validation F1 0.7895\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0312 | Training Loss 0.4235 | Training F1 0.8214 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0312 | Training Loss 0.4208 | Training F1 0.8247 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0312 | Training Loss 0.4196 | Training F1 0.8166 | Validation Accuracy 0.7738 | Validation F1 0.7894\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0312 | Training Loss 0.4194 | Training F1 0.8231 | Validation Accuracy 0.7890 | Validation F1 0.8159\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0312 | Training Loss 0.4193 | Training F1 0.8169 | Validation Accuracy 0.7833 | Validation F1 0.7964\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0312 | Training Loss 0.4182 | Training F1 0.8249 | Validation Accuracy 0.7890 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0313 | Training Loss 0.4160 | Training F1 0.8208 | Validation Accuracy 0.7814 | Validation F1 0.7993\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0313 | Training Loss 0.4135 | Training F1 0.8242 | Validation Accuracy 0.7833 | Validation F1 0.8048\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0313 | Training Loss 0.4117 | Training F1 0.8255 | Validation Accuracy 0.7871 | Validation F1 0.8108\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0313 | Training Loss 0.4111 | Training F1 0.8224 | Validation Accuracy 0.7852 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0313 | Training Loss 0.4112 | Training F1 0.8289 | Validation Accuracy 0.7814 | Validation F1 0.8093\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0313 | Training Loss 0.4117 | Training F1 0.8226 | Validation Accuracy 0.7871 | Validation F1 0.7978\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0313 | Training Loss 0.4119 | Training F1 0.8288 | Validation Accuracy 0.7833 | Validation F1 0.8113\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0313 | Training Loss 0.4104 | Training F1 0.8255 | Validation Accuracy 0.7928 | Validation F1 0.8071\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0313 | Training Loss 0.4070 | Training F1 0.8288 | Validation Accuracy 0.7871 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0313 | Training Loss 0.4040 | Training F1 0.8278 | Validation Accuracy 0.7795 | Validation F1 0.8041\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0313 | Training Loss 0.4033 | Training F1 0.8256 | Validation Accuracy 0.7985 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0313 | Training Loss 0.4039 | Training F1 0.8282 | Validation Accuracy 0.7795 | Validation F1 0.8079\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0313 | Training Loss 0.4042 | Training F1 0.8259 | Validation Accuracy 0.7947 | Validation F1 0.8078\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0313 | Training Loss 0.4030 | Training F1 0.8291 | Validation Accuracy 0.7947 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0312 | Training Loss 0.4004 | Training F1 0.8319 | Validation Accuracy 0.7966 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0312 | Training Loss 0.3978 | Training F1 0.8311 | Validation Accuracy 0.7947 | Validation F1 0.8125\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0312 | Training Loss 0.3972 | Training F1 0.8306 | Validation Accuracy 0.7928 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0312 | Training Loss 0.3976 | Training F1 0.8265 | Validation Accuracy 0.7966 | Validation F1 0.8093\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0312 | Training Loss 0.3984 | Training F1 0.8333 | Validation Accuracy 0.7890 | Validation F1 0.8159\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0312 | Training Loss 0.3986 | Training F1 0.8275 | Validation Accuracy 0.7985 | Validation F1 0.8100\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0312 | Training Loss 0.3974 | Training F1 0.8342 | Validation Accuracy 0.7947 | Validation F1 0.8200\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0312 | Training Loss 0.3959 | Training F1 0.8327 | Validation Accuracy 0.7947 | Validation F1 0.8092\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0312 | Training Loss 0.3928 | Training F1 0.8339 | Validation Accuracy 0.8023 | Validation F1 0.8219\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0312 | Training Loss 0.3900 | Training F1 0.8340 | Validation Accuracy 0.7985 | Validation F1 0.8203\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0312 | Training Loss 0.3895 | Training F1 0.8333 | Validation Accuracy 0.7947 | Validation F1 0.8085\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0312 | Training Loss 0.3903 | Training F1 0.8378 | Validation Accuracy 0.7928 | Validation F1 0.8186\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0312 | Training Loss 0.3914 | Training F1 0.8298 | Validation Accuracy 0.7947 | Validation F1 0.8071\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0312 | Training Loss 0.3914 | Training F1 0.8396 | Validation Accuracy 0.7909 | Validation F1 0.8173\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0312 | Training Loss 0.3898 | Training F1 0.8342 | Validation Accuracy 0.8023 | Validation F1 0.8163\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0312 | Training Loss 0.3866 | Training F1 0.8398 | Validation Accuracy 0.7966 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0312 | Training Loss 0.3841 | Training F1 0.8397 | Validation Accuracy 0.8004 | Validation F1 0.8187\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0312 | Training Loss 0.3825 | Training F1 0.8391 | Validation Accuracy 0.7985 | Validation F1 0.8140\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0312 | Training Loss 0.3821 | Training F1 0.8401 | Validation Accuracy 0.7947 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0312 | Training Loss 0.3827 | Training F1 0.8386 | Validation Accuracy 0.7947 | Validation F1 0.8071\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7f9f68416350>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0382 | Training Loss 10.4631 | Training F1 0.7019 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0356 | Training Loss 4.6603 | Training F1 0.6140 | Validation Accuracy 0.6103 | Validation F1 0.6346\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0346 | Training Loss 0.9228 | Training F1 0.0842 | Validation Accuracy 0.4772 | Validation F1 0.0984\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0342 | Training Loss 1.5583 | Training F1 0.6919 | Validation Accuracy 0.5837 | Validation F1 0.6902\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0339 | Training Loss 0.8007 | Training F1 0.7017 | Validation Accuracy 0.5513 | Validation F1 0.7043\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0336 | Training Loss 1.2549 | Training F1 0.7058 | Validation Accuracy 0.5894 | Validation F1 0.7158\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0334 | Training Loss 0.9242 | Training F1 0.5328 | Validation Accuracy 0.6369 | Validation F1 0.5765\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0333 | Training Loss 0.8010 | Training F1 0.6613 | Validation Accuracy 0.6920 | Validation F1 0.7128\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0331 | Training Loss 0.6769 | Training F1 0.7156 | Validation Accuracy 0.6426 | Validation F1 0.7360\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0330 | Training Loss 0.6878 | Training F1 0.6906 | Validation Accuracy 0.6635 | Validation F1 0.7204\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0328 | Training Loss 0.6497 | Training F1 0.6010 | Validation Accuracy 0.6274 | Validation F1 0.6111\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0327 | Training Loss 0.6505 | Training F1 0.5734 | Validation Accuracy 0.6084 | Validation F1 0.5690\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0325 | Training Loss 0.6529 | Training F1 0.6777 | Validation Accuracy 0.6616 | Validation F1 0.6910\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0324 | Training Loss 0.6221 | Training F1 0.7292 | Validation Accuracy 0.6654 | Validation F1 0.7396\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0322 | Training Loss 0.6185 | Training F1 0.7396 | Validation Accuracy 0.6635 | Validation F1 0.7378\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0321 | Training Loss 0.6051 | Training F1 0.7295 | Validation Accuracy 0.6958 | Validation F1 0.7315\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0320 | Training Loss 0.5821 | Training F1 0.6715 | Validation Accuracy 0.6977 | Validation F1 0.6994\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0320 | Training Loss 0.5936 | Training F1 0.7395 | Validation Accuracy 0.7072 | Validation F1 0.7390\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0319 | Training Loss 0.5679 | Training F1 0.7604 | Validation Accuracy 0.6825 | Validation F1 0.7489\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0318 | Training Loss 0.5761 | Training F1 0.7651 | Validation Accuracy 0.6996 | Validation F1 0.7508\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0318 | Training Loss 0.5588 | Training F1 0.7198 | Validation Accuracy 0.7243 | Validation F1 0.7349\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0317 | Training Loss 0.5558 | Training F1 0.7299 | Validation Accuracy 0.7243 | Validation F1 0.7359\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0317 | Training Loss 0.5475 | Training F1 0.7743 | Validation Accuracy 0.7338 | Validation F1 0.7735\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0316 | Training Loss 0.5377 | Training F1 0.7760 | Validation Accuracy 0.7205 | Validation F1 0.7663\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0316 | Training Loss 0.5364 | Training F1 0.7480 | Validation Accuracy 0.7433 | Validation F1 0.7636\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0315 | Training Loss 0.5255 | Training F1 0.7426 | Validation Accuracy 0.7414 | Validation F1 0.7563\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0315 | Training Loss 0.5242 | Training F1 0.7761 | Validation Accuracy 0.7567 | Validation F1 0.7881\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0316 | Training Loss 0.5160 | Training F1 0.7810 | Validation Accuracy 0.7548 | Validation F1 0.7936\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0316 | Training Loss 0.5156 | Training F1 0.7647 | Validation Accuracy 0.7624 | Validation F1 0.7841\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0315 | Training Loss 0.5083 | Training F1 0.7681 | Validation Accuracy 0.7662 | Validation F1 0.7876\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0315 | Training Loss 0.5055 | Training F1 0.7785 | Validation Accuracy 0.7719 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0315 | Training Loss 0.5014 | Training F1 0.7814 | Validation Accuracy 0.7662 | Validation F1 0.7987\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0314 | Training Loss 0.4976 | Training F1 0.7767 | Validation Accuracy 0.7776 | Validation F1 0.7993\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0314 | Training Loss 0.4943 | Training F1 0.7809 | Validation Accuracy 0.7833 | Validation F1 0.8048\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0314 | Training Loss 0.4907 | Training F1 0.7908 | Validation Accuracy 0.7700 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0314 | Training Loss 0.4879 | Training F1 0.7915 | Validation Accuracy 0.7757 | Validation F1 0.8059\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0313 | Training Loss 0.4833 | Training F1 0.7847 | Validation Accuracy 0.7871 | Validation F1 0.8069\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0313 | Training Loss 0.4817 | Training F1 0.7927 | Validation Accuracy 0.7871 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0313 | Training Loss 0.4772 | Training F1 0.8017 | Validation Accuracy 0.7738 | Validation F1 0.8059\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0313 | Training Loss 0.4764 | Training F1 0.7960 | Validation Accuracy 0.7871 | Validation F1 0.8121\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0313 | Training Loss 0.4730 | Training F1 0.7898 | Validation Accuracy 0.7890 | Validation F1 0.8083\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0312 | Training Loss 0.4716 | Training F1 0.7997 | Validation Accuracy 0.7795 | Validation F1 0.8098\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0312 | Training Loss 0.4687 | Training F1 0.8031 | Validation Accuracy 0.7776 | Validation F1 0.8027\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0312 | Training Loss 0.4641 | Training F1 0.7895 | Validation Accuracy 0.7890 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0312 | Training Loss 0.4639 | Training F1 0.8059 | Validation Accuracy 0.7757 | Validation F1 0.8027\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0312 | Training Loss 0.4608 | Training F1 0.8033 | Validation Accuracy 0.7833 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0312 | Training Loss 0.4573 | Training F1 0.7966 | Validation Accuracy 0.7890 | Validation F1 0.8083\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0312 | Training Loss 0.4569 | Training F1 0.8074 | Validation Accuracy 0.7719 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0311 | Training Loss 0.4549 | Training F1 0.8033 | Validation Accuracy 0.7871 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0311 | Training Loss 0.4504 | Training F1 0.8053 | Validation Accuracy 0.7909 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0311 | Training Loss 0.4496 | Training F1 0.8097 | Validation Accuracy 0.7681 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0311 | Training Loss 0.4500 | Training F1 0.8023 | Validation Accuracy 0.7966 | Validation F1 0.8139\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0311 | Training Loss 0.4483 | Training F1 0.8112 | Validation Accuracy 0.7738 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0311 | Training Loss 0.4430 | Training F1 0.8144 | Validation Accuracy 0.7833 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0311 | Training Loss 0.4404 | Training F1 0.8085 | Validation Accuracy 0.7947 | Validation F1 0.8131\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0311 | Training Loss 0.4409 | Training F1 0.8153 | Validation Accuracy 0.7757 | Validation F1 0.8053\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0311 | Training Loss 0.4387 | Training F1 0.8143 | Validation Accuracy 0.7928 | Validation F1 0.8124\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0311 | Training Loss 0.4351 | Training F1 0.8132 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0310 | Training Loss 0.4322 | Training F1 0.8171 | Validation Accuracy 0.7852 | Validation F1 0.8101\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0311 | Training Loss 0.4306 | Training F1 0.8072 | Validation Accuracy 0.8099 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0311 | Training Loss 0.4322 | Training F1 0.8145 | Validation Accuracy 0.7776 | Validation F1 0.8140\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0311 | Training Loss 0.4422 | Training F1 0.7776 | Validation Accuracy 0.7833 | Validation F1 0.7849\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0311 | Training Loss 0.4567 | Training F1 0.8180 | Validation Accuracy 0.7738 | Validation F1 0.8144\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0311 | Training Loss 0.4545 | Training F1 0.8102 | Validation Accuracy 0.8099 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0311 | Training Loss 0.4265 | Training F1 0.8092 | Validation Accuracy 0.8118 | Validation F1 0.8242\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0311 | Training Loss 0.4273 | Training F1 0.8230 | Validation Accuracy 0.7738 | Validation F1 0.8132\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0310 | Training Loss 0.4424 | Training F1 0.8098 | Validation Accuracy 0.8137 | Validation F1 0.8256\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0310 | Training Loss 0.4249 | Training F1 0.8151 | Validation Accuracy 0.8061 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0310 | Training Loss 0.4185 | Training F1 0.8271 | Validation Accuracy 0.7871 | Validation F1 0.8205\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0310 | Training Loss 0.4314 | Training F1 0.8062 | Validation Accuracy 0.8080 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0310 | Training Loss 0.4222 | Training F1 0.8235 | Validation Accuracy 0.8004 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0310 | Training Loss 0.4124 | Training F1 0.8255 | Validation Accuracy 0.7928 | Validation F1 0.8250\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0310 | Training Loss 0.4241 | Training F1 0.7994 | Validation Accuracy 0.8042 | Validation F1 0.8124\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0310 | Training Loss 0.4303 | Training F1 0.8296 | Validation Accuracy 0.7947 | Validation F1 0.8218\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0310 | Training Loss 0.4134 | Training F1 0.8321 | Validation Accuracy 0.7928 | Validation F1 0.8198\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0310 | Training Loss 0.4109 | Training F1 0.8046 | Validation Accuracy 0.8042 | Validation F1 0.8151\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0310 | Training Loss 0.4198 | Training F1 0.8329 | Validation Accuracy 0.7985 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0310 | Training Loss 0.4090 | Training F1 0.8326 | Validation Accuracy 0.8042 | Validation F1 0.8269\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0310 | Training Loss 0.4050 | Training F1 0.8145 | Validation Accuracy 0.8080 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0310 | Training Loss 0.4103 | Training F1 0.8328 | Validation Accuracy 0.8004 | Validation F1 0.8253\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0311 | Training Loss 0.4037 | Training F1 0.8340 | Validation Accuracy 0.8080 | Validation F1 0.8303\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0311 | Training Loss 0.4009 | Training F1 0.8187 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0311 | Training Loss 0.4049 | Training F1 0.8316 | Validation Accuracy 0.8042 | Validation F1 0.8280\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0311 | Training Loss 0.4003 | Training F1 0.8313 | Validation Accuracy 0.8042 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0311 | Training Loss 0.3963 | Training F1 0.8222 | Validation Accuracy 0.8099 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0311 | Training Loss 0.3991 | Training F1 0.8346 | Validation Accuracy 0.8023 | Validation F1 0.8272\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0311 | Training Loss 0.3975 | Training F1 0.8296 | Validation Accuracy 0.8042 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0311 | Training Loss 0.3926 | Training F1 0.8268 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0311 | Training Loss 0.3935 | Training F1 0.8360 | Validation Accuracy 0.8004 | Validation F1 0.8270\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0311 | Training Loss 0.3951 | Training F1 0.8303 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0311 | Training Loss 0.3913 | Training F1 0.8343 | Validation Accuracy 0.8004 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0311 | Training Loss 0.3880 | Training F1 0.8357 | Validation Accuracy 0.8023 | Validation F1 0.8261\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0311 | Training Loss 0.3885 | Training F1 0.8316 | Validation Accuracy 0.8042 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0311 | Training Loss 0.3887 | Training F1 0.8352 | Validation Accuracy 0.8023 | Validation F1 0.8267\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0311 | Training Loss 0.3867 | Training F1 0.8349 | Validation Accuracy 0.8080 | Validation F1 0.8274\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0311 | Training Loss 0.3840 | Training F1 0.8366 | Validation Accuracy 0.8042 | Validation F1 0.8251\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0311 | Training Loss 0.3828 | Training F1 0.8371 | Validation Accuracy 0.8023 | Validation F1 0.8261\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0311 | Training Loss 0.3825 | Training F1 0.8347 | Validation Accuracy 0.8080 | Validation F1 0.8256\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0311 | Training Loss 0.3824 | Training F1 0.8355 | Validation Accuracy 0.8042 | Validation F1 0.8298\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0311 | Training Loss 0.3821 | Training F1 0.8346 | Validation Accuracy 0.8061 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0311 | Training Loss 0.3817 | Training F1 0.8383 | Validation Accuracy 0.8023 | Validation F1 0.8289\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0311 | Training Loss 0.3804 | Training F1 0.8333 | Validation Accuracy 0.8023 | Validation F1 0.8201\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0311 | Training Loss 0.3793 | Training F1 0.8401 | Validation Accuracy 0.7966 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0311 | Training Loss 0.3784 | Training F1 0.8362 | Validation Accuracy 0.8080 | Validation F1 0.8256\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0311 | Training Loss 0.3766 | Training F1 0.8388 | Validation Accuracy 0.8023 | Validation F1 0.8278\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0311 | Training Loss 0.3764 | Training F1 0.8339 | Validation Accuracy 0.8099 | Validation F1 0.8258\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0311 | Training Loss 0.3762 | Training F1 0.8434 | Validation Accuracy 0.8004 | Validation F1 0.8287\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0311 | Training Loss 0.3770 | Training F1 0.8339 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0311 | Training Loss 0.3796 | Training F1 0.8451 | Validation Accuracy 0.7985 | Validation F1 0.8296\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0311 | Training Loss 0.3850 | Training F1 0.8208 | Validation Accuracy 0.7985 | Validation F1 0.8073\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0311 | Training Loss 0.3926 | Training F1 0.8366 | Validation Accuracy 0.7795 | Validation F1 0.8182\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0311 | Training Loss 0.4045 | Training F1 0.8019 | Validation Accuracy 0.7985 | Validation F1 0.8022\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0310 | Training Loss 0.4058 | Training F1 0.8420 | Validation Accuracy 0.7833 | Validation F1 0.8208\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0310 | Training Loss 0.3988 | Training F1 0.8381 | Validation Accuracy 0.8099 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0310 | Training Loss 0.3713 | Training F1 0.8407 | Validation Accuracy 0.8099 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0310 | Training Loss 0.3700 | Training F1 0.8447 | Validation Accuracy 0.7871 | Validation F1 0.8228\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0310 | Training Loss 0.3875 | Training F1 0.8270 | Validation Accuracy 0.8061 | Validation F1 0.8159\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0310 | Training Loss 0.3819 | Training F1 0.8452 | Validation Accuracy 0.8099 | Validation F1 0.8344\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0310 | Training Loss 0.3658 | Training F1 0.8469 | Validation Accuracy 0.8118 | Validation F1 0.8374\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7f9f68416430>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0515 | Training Loss 18.4976 | Training F1 0.6670 | Validation Accuracy 0.6255 | Validation F1 0.6609\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0429 | Training Loss 0.7369 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0393 | Training Loss 4.7968 | Training F1 0.5435 | Validation Accuracy 0.6179 | Validation F1 0.5463\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0373 | Training Loss 0.7040 | Training F1 0.7025 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0361 | Training Loss 2.7197 | Training F1 0.7023 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0353 | Training Loss 2.1343 | Training F1 0.7109 | Validation Accuracy 0.5856 | Validation F1 0.7219\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0347 | Training Loss 0.7847 | Training F1 0.0087 | Validation Accuracy 0.4620 | Validation F1 0.0139\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0343 | Training Loss 1.1205 | Training F1 0.0018 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0339 | Training Loss 1.3902 | Training F1 0.0139 | Validation Accuracy 0.4620 | Validation F1 0.0275\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0337 | Training Loss 0.9782 | Training F1 0.5645 | Validation Accuracy 0.5932 | Validation F1 0.5579\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0334 | Training Loss 0.6686 | Training F1 0.7194 | Validation Accuracy 0.6179 | Validation F1 0.7287\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0332 | Training Loss 0.6647 | Training F1 0.7048 | Validation Accuracy 0.5760 | Validation F1 0.7188\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0330 | Training Loss 0.7627 | Training F1 0.7046 | Validation Accuracy 0.5608 | Validation F1 0.7116\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0329 | Training Loss 0.7901 | Training F1 0.7065 | Validation Accuracy 0.5703 | Validation F1 0.7161\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0328 | Training Loss 0.7468 | Training F1 0.7170 | Validation Accuracy 0.5970 | Validation F1 0.7261\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0327 | Training Loss 0.6759 | Training F1 0.7265 | Validation Accuracy 0.6388 | Validation F1 0.7339\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0326 | Training Loss 0.6240 | Training F1 0.7013 | Validation Accuracy 0.6920 | Validation F1 0.7273\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0325 | Training Loss 0.6172 | Training F1 0.5752 | Validation Accuracy 0.6502 | Validation F1 0.6052\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0325 | Training Loss 0.6431 | Training F1 0.4656 | Validation Accuracy 0.6027 | Validation F1 0.4865\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0324 | Training Loss 0.6645 | Training F1 0.4781 | Validation Accuracy 0.6198 | Validation F1 0.5192\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0323 | Training Loss 0.6602 | Training F1 0.6036 | Validation Accuracy 0.6597 | Validation F1 0.6183\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0323 | Training Loss 0.6337 | Training F1 0.7263 | Validation Accuracy 0.7319 | Validation F1 0.7638\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0322 | Training Loss 0.6046 | Training F1 0.7465 | Validation Accuracy 0.6825 | Validation F1 0.7631\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0322 | Training Loss 0.6061 | Training F1 0.7411 | Validation Accuracy 0.6464 | Validation F1 0.7473\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0321 | Training Loss 0.6149 | Training F1 0.7456 | Validation Accuracy 0.6597 | Validation F1 0.7524\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0323 | Training Loss 0.6078 | Training F1 0.7559 | Validation Accuracy 0.6939 | Validation F1 0.7683\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0322 | Training Loss 0.5955 | Training F1 0.7613 | Validation Accuracy 0.7110 | Validation F1 0.7738\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0322 | Training Loss 0.5869 | Training F1 0.7553 | Validation Accuracy 0.7471 | Validation F1 0.7892\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0321 | Training Loss 0.5817 | Training F1 0.7492 | Validation Accuracy 0.7490 | Validation F1 0.7843\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0321 | Training Loss 0.5758 | Training F1 0.7548 | Validation Accuracy 0.7567 | Validation F1 0.7942\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0320 | Training Loss 0.5673 | Training F1 0.7677 | Validation Accuracy 0.7490 | Validation F1 0.7931\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0320 | Training Loss 0.5573 | Training F1 0.7526 | Validation Accuracy 0.7700 | Validation F1 0.7987\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0320 | Training Loss 0.5475 | Training F1 0.7607 | Validation Accuracy 0.7738 | Validation F1 0.8033\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0320 | Training Loss 0.5382 | Training F1 0.7727 | Validation Accuracy 0.7605 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0319 | Training Loss 0.5330 | Training F1 0.7449 | Validation Accuracy 0.7605 | Validation F1 0.7726\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0319 | Training Loss 0.5320 | Training F1 0.7731 | Validation Accuracy 0.7643 | Validation F1 0.7980\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0318 | Training Loss 0.5190 | Training F1 0.7766 | Validation Accuracy 0.7605 | Validation F1 0.7981\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0318 | Training Loss 0.5172 | Training F1 0.7621 | Validation Accuracy 0.7795 | Validation F1 0.7965\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0317 | Training Loss 0.5124 | Training F1 0.7696 | Validation Accuracy 0.7833 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0317 | Training Loss 0.5059 | Training F1 0.7804 | Validation Accuracy 0.7738 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0317 | Training Loss 0.5043 | Training F1 0.7793 | Validation Accuracy 0.7814 | Validation F1 0.8048\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0316 | Training Loss 0.4962 | Training F1 0.7742 | Validation Accuracy 0.7795 | Validation F1 0.7929\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0316 | Training Loss 0.4966 | Training F1 0.7796 | Validation Accuracy 0.7852 | Validation F1 0.8107\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0315 | Training Loss 0.4911 | Training F1 0.7826 | Validation Accuracy 0.7833 | Validation F1 0.8106\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0315 | Training Loss 0.4903 | Training F1 0.7781 | Validation Accuracy 0.7833 | Validation F1 0.7972\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0315 | Training Loss 0.4885 | Training F1 0.7886 | Validation Accuracy 0.7890 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0314 | Training Loss 0.4831 | Training F1 0.7890 | Validation Accuracy 0.7814 | Validation F1 0.8093\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0314 | Training Loss 0.4830 | Training F1 0.7808 | Validation Accuracy 0.7947 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0314 | Training Loss 0.4798 | Training F1 0.7881 | Validation Accuracy 0.7909 | Validation F1 0.8103\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0314 | Training Loss 0.4755 | Training F1 0.7931 | Validation Accuracy 0.7852 | Validation F1 0.8126\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0313 | Training Loss 0.4750 | Training F1 0.7797 | Validation Accuracy 0.7928 | Validation F1 0.8091\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0313 | Training Loss 0.4722 | Training F1 0.7939 | Validation Accuracy 0.7890 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0313 | Training Loss 0.4678 | Training F1 0.7974 | Validation Accuracy 0.7947 | Validation F1 0.8188\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0313 | Training Loss 0.4660 | Training F1 0.7813 | Validation Accuracy 0.7852 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0312 | Training Loss 0.4653 | Training F1 0.8015 | Validation Accuracy 0.7928 | Validation F1 0.8180\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0312 | Training Loss 0.4634 | Training F1 0.7888 | Validation Accuracy 0.7795 | Validation F1 0.7951\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0312 | Training Loss 0.4605 | Training F1 0.7958 | Validation Accuracy 0.7928 | Validation F1 0.8137\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0312 | Training Loss 0.4573 | Training F1 0.8023 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0311 | Training Loss 0.4553 | Training F1 0.7894 | Validation Accuracy 0.7871 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0311 | Training Loss 0.4553 | Training F1 0.8043 | Validation Accuracy 0.7909 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0311 | Training Loss 0.4570 | Training F1 0.7754 | Validation Accuracy 0.7757 | Validation F1 0.7831\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0311 | Training Loss 0.4582 | Training F1 0.8069 | Validation Accuracy 0.7928 | Validation F1 0.8198\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0311 | Training Loss 0.4547 | Training F1 0.7993 | Validation Accuracy 0.7871 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0311 | Training Loss 0.4472 | Training F1 0.8025 | Validation Accuracy 0.7909 | Validation F1 0.8070\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0312 | Training Loss 0.4452 | Training F1 0.8081 | Validation Accuracy 0.7909 | Validation F1 0.8173\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0311 | Training Loss 0.4475 | Training F1 0.7987 | Validation Accuracy 0.7852 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0311 | Training Loss 0.4440 | Training F1 0.8106 | Validation Accuracy 0.8023 | Validation F1 0.8219\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0311 | Training Loss 0.4389 | Training F1 0.8133 | Validation Accuracy 0.7890 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0311 | Training Loss 0.4401 | Training F1 0.8018 | Validation Accuracy 0.7776 | Validation F1 0.7899\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0311 | Training Loss 0.4412 | Training F1 0.8137 | Validation Accuracy 0.7909 | Validation F1 0.8161\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0311 | Training Loss 0.4367 | Training F1 0.8139 | Validation Accuracy 0.7966 | Validation F1 0.8165\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0310 | Training Loss 0.4329 | Training F1 0.8104 | Validation Accuracy 0.7947 | Validation F1 0.8105\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0310 | Training Loss 0.4330 | Training F1 0.8135 | Validation Accuracy 0.7966 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0310 | Training Loss 0.4335 | Training F1 0.8078 | Validation Accuracy 0.7928 | Validation F1 0.8071\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0310 | Training Loss 0.4319 | Training F1 0.8139 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0310 | Training Loss 0.4283 | Training F1 0.8123 | Validation Accuracy 0.8023 | Validation F1 0.8219\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0310 | Training Loss 0.4260 | Training F1 0.8161 | Validation Accuracy 0.7947 | Validation F1 0.8112\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0310 | Training Loss 0.4255 | Training F1 0.8138 | Validation Accuracy 0.7966 | Validation F1 0.8208\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0310 | Training Loss 0.4256 | Training F1 0.8097 | Validation Accuracy 0.7966 | Validation F1 0.8086\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0310 | Training Loss 0.4262 | Training F1 0.8199 | Validation Accuracy 0.7947 | Validation F1 0.8224\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0309 | Training Loss 0.4289 | Training F1 0.7987 | Validation Accuracy 0.8004 | Validation F1 0.8059\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0310 | Training Loss 0.4350 | Training F1 0.8197 | Validation Accuracy 0.7871 | Validation F1 0.8188\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0309 | Training Loss 0.4331 | Training F1 0.8075 | Validation Accuracy 0.7985 | Validation F1 0.8100\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0309 | Training Loss 0.4231 | Training F1 0.8192 | Validation Accuracy 0.7985 | Validation F1 0.8172\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0309 | Training Loss 0.4178 | Training F1 0.8187 | Validation Accuracy 0.7871 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0309 | Training Loss 0.4230 | Training F1 0.8030 | Validation Accuracy 0.8042 | Validation F1 0.8131\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0309 | Training Loss 0.4233 | Training F1 0.8202 | Validation Accuracy 0.7928 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0309 | Training Loss 0.4169 | Training F1 0.8186 | Validation Accuracy 0.8023 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0309 | Training Loss 0.4140 | Training F1 0.8084 | Validation Accuracy 0.8004 | Validation F1 0.8115\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0309 | Training Loss 0.4168 | Training F1 0.8229 | Validation Accuracy 0.8004 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0309 | Training Loss 0.4169 | Training F1 0.8144 | Validation Accuracy 0.8004 | Validation F1 0.8135\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0309 | Training Loss 0.4136 | Training F1 0.8216 | Validation Accuracy 0.8042 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0308 | Training Loss 0.4094 | Training F1 0.8236 | Validation Accuracy 0.7966 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0308 | Training Loss 0.4089 | Training F1 0.8186 | Validation Accuracy 0.8042 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0308 | Training Loss 0.4107 | Training F1 0.8239 | Validation Accuracy 0.7985 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0308 | Training Loss 0.4120 | Training F1 0.8178 | Validation Accuracy 0.8042 | Validation F1 0.8171\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0308 | Training Loss 0.4110 | Training F1 0.8285 | Validation Accuracy 0.7947 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0308 | Training Loss 0.4074 | Training F1 0.8191 | Validation Accuracy 0.8004 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0308 | Training Loss 0.4043 | Training F1 0.8229 | Validation Accuracy 0.7928 | Validation F1 0.8124\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0308 | Training Loss 0.4027 | Training F1 0.8300 | Validation Accuracy 0.8023 | Validation F1 0.8255\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0308 | Training Loss 0.4031 | Training F1 0.8213 | Validation Accuracy 0.8004 | Validation F1 0.8142\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0308 | Training Loss 0.4040 | Training F1 0.8310 | Validation Accuracy 0.7985 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0308 | Training Loss 0.4047 | Training F1 0.8167 | Validation Accuracy 0.7985 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0308 | Training Loss 0.4074 | Training F1 0.8263 | Validation Accuracy 0.7928 | Validation F1 0.8222\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0308 | Training Loss 0.4111 | Training F1 0.8097 | Validation Accuracy 0.8061 | Validation F1 0.8111\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0308 | Training Loss 0.4165 | Training F1 0.8273 | Validation Accuracy 0.7947 | Validation F1 0.8252\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0308 | Training Loss 0.4154 | Training F1 0.8201 | Validation Accuracy 0.7966 | Validation F1 0.8079\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0308 | Training Loss 0.4054 | Training F1 0.8289 | Validation Accuracy 0.8004 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0308 | Training Loss 0.3966 | Training F1 0.8295 | Validation Accuracy 0.8023 | Validation F1 0.8278\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0308 | Training Loss 0.4013 | Training F1 0.8154 | Validation Accuracy 0.8042 | Validation F1 0.8137\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0308 | Training Loss 0.4074 | Training F1 0.8323 | Validation Accuracy 0.7985 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0308 | Training Loss 0.4044 | Training F1 0.8241 | Validation Accuracy 0.8042 | Validation F1 0.8202\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0307 | Training Loss 0.3958 | Training F1 0.8242 | Validation Accuracy 0.8061 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0307 | Training Loss 0.3946 | Training F1 0.8341 | Validation Accuracy 0.7985 | Validation F1 0.8251\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0307 | Training Loss 0.4009 | Training F1 0.8211 | Validation Accuracy 0.8004 | Validation F1 0.8108\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0307 | Training Loss 0.4031 | Training F1 0.8362 | Validation Accuracy 0.7985 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0307 | Training Loss 0.3978 | Training F1 0.8264 | Validation Accuracy 0.8099 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0307 | Training Loss 0.3917 | Training F1 0.8260 | Validation Accuracy 0.8099 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0307 | Training Loss 0.3911 | Training F1 0.8329 | Validation Accuracy 0.8004 | Validation F1 0.8259\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Numpy version:1.24.2 Pytorch version:1.12.1+cpu DGL version:1.0.0\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Read train and test dataset.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Further slice the train dataset into train and validation datasets.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The training data has shape: (2102, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The validation data has shape: (526, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:The test data has shape: (657, 16).\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Generate train, validation, and test masks.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of nodes = 3285\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of features for each node = 14\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Number of classes = 2.\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initializing Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Initialized Model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:<generator object Module.parameters at 0x7f9f68416430>\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0389 | Training Loss 3.0370 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0348 | Training Loss 23.8445 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0334 | Training Loss 14.3688 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0326 | Training Loss 3.0089 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0321 | Training Loss 6.5376 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0317 | Training Loss 7.2045 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0323 | Training Loss 5.2665 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0336 | Training Loss 2.5684 | Training F1 0.5679 | Validation Accuracy 0.5437 | Validation F1 0.5668\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0333 | Training Loss 0.6918 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0330 | Training Loss 1.4374 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0329 | Training Loss 1.7213 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0327 | Training Loss 1.4861 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0325 | Training Loss 1.0977 | Training F1 0.7017 | Validation Accuracy 0.5418 | Validation F1 0.7021\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0324 | Training Loss 0.7191 | Training F1 0.0428 | Validation Accuracy 0.4658 | Validation F1 0.0344\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0322 | Training Loss 0.7732 | Training F1 0.0018 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0322 | Training Loss 1.0198 | Training F1 0.0018 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0321 | Training Loss 0.9804 | Training F1 0.0087 | Validation Accuracy 0.4582 | Validation F1 0.0070\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0320 | Training Loss 0.7951 | Training F1 0.4917 | Validation Accuracy 0.5741 | Validation F1 0.4766\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0319 | Training Loss 0.6789 | Training F1 0.7080 | Validation Accuracy 0.5760 | Validation F1 0.7115\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0319 | Training Loss 0.6655 | Training F1 0.7039 | Validation Accuracy 0.5589 | Validation F1 0.7107\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0318 | Training Loss 0.6994 | Training F1 0.7028 | Validation Accuracy 0.5494 | Validation F1 0.7063\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0318 | Training Loss 0.7192 | Training F1 0.7049 | Validation Accuracy 0.5551 | Validation F1 0.7090\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0317 | Training Loss 0.7066 | Training F1 0.7109 | Validation Accuracy 0.5722 | Validation F1 0.7155\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0317 | Training Loss 0.6761 | Training F1 0.7261 | Validation Accuracy 0.6122 | Validation F1 0.7302\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0316 | Training Loss 0.6481 | Training F1 0.7228 | Validation Accuracy 0.6540 | Validation F1 0.7267\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0316 | Training Loss 0.6353 | Training F1 0.6472 | Validation Accuracy 0.6616 | Validation F1 0.6603\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0316 | Training Loss 0.6416 | Training F1 0.5244 | Validation Accuracy 0.6236 | Validation F1 0.5561\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0316 | Training Loss 0.6533 | Training F1 0.5068 | Validation Accuracy 0.6274 | Validation F1 0.5505\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0315 | Training Loss 0.6544 | Training F1 0.5841 | Validation Accuracy 0.6445 | Validation F1 0.6080\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0315 | Training Loss 0.6429 | Training F1 0.6761 | Validation Accuracy 0.6825 | Validation F1 0.6855\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0315 | Training Loss 0.6286 | Training F1 0.7305 | Validation Accuracy 0.6863 | Validation F1 0.7343\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0314 | Training Loss 0.6192 | Training F1 0.7360 | Validation Accuracy 0.6787 | Validation F1 0.7547\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0314 | Training Loss 0.6181 | Training F1 0.7382 | Validation Accuracy 0.6654 | Validation F1 0.7542\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0314 | Training Loss 0.6196 | Training F1 0.7396 | Validation Accuracy 0.6692 | Validation F1 0.7556\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0314 | Training Loss 0.6159 | Training F1 0.7438 | Validation Accuracy 0.7015 | Validation F1 0.7639\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0314 | Training Loss 0.6038 | Training F1 0.7056 | Validation Accuracy 0.7224 | Validation F1 0.7374\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0313 | Training Loss 0.5978 | Training F1 0.7011 | Validation Accuracy 0.7129 | Validation F1 0.7188\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0313 | Training Loss 0.5930 | Training F1 0.7353 | Validation Accuracy 0.7205 | Validation F1 0.7538\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0314 | Training Loss 0.5797 | Training F1 0.7505 | Validation Accuracy 0.7167 | Validation F1 0.7624\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0314 | Training Loss 0.5750 | Training F1 0.7496 | Validation Accuracy 0.7262 | Validation F1 0.7692\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0314 | Training Loss 0.5689 | Training F1 0.7448 | Validation Accuracy 0.7529 | Validation F1 0.7819\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0313 | Training Loss 0.5603 | Training F1 0.7169 | Validation Accuracy 0.7433 | Validation F1 0.7568\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0313 | Training Loss 0.5581 | Training F1 0.7296 | Validation Accuracy 0.7624 | Validation F1 0.7795\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0313 | Training Loss 0.5507 | Training F1 0.7513 | Validation Accuracy 0.7605 | Validation F1 0.7914\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0313 | Training Loss 0.5459 | Training F1 0.7572 | Validation Accuracy 0.7567 | Validation F1 0.7908\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0313 | Training Loss 0.5436 | Training F1 0.7489 | Validation Accuracy 0.7681 | Validation F1 0.7939\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0313 | Training Loss 0.5382 | Training F1 0.7392 | Validation Accuracy 0.7567 | Validation F1 0.7739\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0312 | Training Loss 0.5360 | Training F1 0.7416 | Validation Accuracy 0.7586 | Validation F1 0.7760\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0312 | Training Loss 0.5326 | Training F1 0.7572 | Validation Accuracy 0.7833 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0312 | Training Loss 0.5287 | Training F1 0.7640 | Validation Accuracy 0.7871 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0312 | Training Loss 0.5276 | Training F1 0.7571 | Validation Accuracy 0.7757 | Validation F1 0.8013\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0312 | Training Loss 0.5239 | Training F1 0.7510 | Validation Accuracy 0.7719 | Validation F1 0.7887\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0312 | Training Loss 0.5229 | Training F1 0.7566 | Validation Accuracy 0.7757 | Validation F1 0.7937\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0312 | Training Loss 0.5203 | Training F1 0.7656 | Validation Accuracy 0.7852 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0312 | Training Loss 0.5185 | Training F1 0.7674 | Validation Accuracy 0.7833 | Validation F1 0.8087\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0312 | Training Loss 0.5170 | Training F1 0.7657 | Validation Accuracy 0.7814 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0312 | Training Loss 0.5145 | Training F1 0.7660 | Validation Accuracy 0.7852 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0312 | Training Loss 0.5132 | Training F1 0.7688 | Validation Accuracy 0.7833 | Validation F1 0.8048\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0312 | Training Loss 0.5105 | Training F1 0.7718 | Validation Accuracy 0.7757 | Validation F1 0.8020\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0311 | Training Loss 0.5089 | Training F1 0.7707 | Validation Accuracy 0.7890 | Validation F1 0.8090\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0311 | Training Loss 0.5063 | Training F1 0.7699 | Validation Accuracy 0.7852 | Validation F1 0.8021\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0311 | Training Loss 0.5046 | Training F1 0.7747 | Validation Accuracy 0.7947 | Validation F1 0.8144\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0311 | Training Loss 0.5022 | Training F1 0.7790 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0311 | Training Loss 0.5004 | Training F1 0.7779 | Validation Accuracy 0.7966 | Validation F1 0.8165\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0311 | Training Loss 0.4981 | Training F1 0.7748 | Validation Accuracy 0.8023 | Validation F1 0.8188\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0311 | Training Loss 0.4964 | Training F1 0.7775 | Validation Accuracy 0.7928 | Validation F1 0.8143\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0311 | Training Loss 0.4939 | Training F1 0.7813 | Validation Accuracy 0.7909 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0311 | Training Loss 0.4924 | Training F1 0.7786 | Validation Accuracy 0.7985 | Validation F1 0.8191\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0311 | Training Loss 0.4903 | Training F1 0.7774 | Validation Accuracy 0.8042 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0311 | Training Loss 0.4889 | Training F1 0.7817 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0311 | Training Loss 0.4868 | Training F1 0.7832 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0311 | Training Loss 0.4852 | Training F1 0.7837 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0311 | Training Loss 0.4827 | Training F1 0.7833 | Validation Accuracy 0.8023 | Validation F1 0.8207\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0311 | Training Loss 0.4808 | Training F1 0.7850 | Validation Accuracy 0.8004 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0311 | Training Loss 0.4789 | Training F1 0.7841 | Validation Accuracy 0.8080 | Validation F1 0.8256\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0311 | Training Loss 0.4766 | Training F1 0.7881 | Validation Accuracy 0.8099 | Validation F1 0.8282\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0311 | Training Loss 0.4744 | Training F1 0.7910 | Validation Accuracy 0.8061 | Validation F1 0.8283\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0311 | Training Loss 0.4731 | Training F1 0.7907 | Validation Accuracy 0.8118 | Validation F1 0.8302\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0311 | Training Loss 0.4716 | Training F1 0.7922 | Validation Accuracy 0.8118 | Validation F1 0.8313\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0311 | Training Loss 0.4700 | Training F1 0.7938 | Validation Accuracy 0.8099 | Validation F1 0.8311\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0311 | Training Loss 0.4687 | Training F1 0.7943 | Validation Accuracy 0.8080 | Validation F1 0.8274\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0311 | Training Loss 0.4669 | Training F1 0.7942 | Validation Accuracy 0.8042 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0311 | Training Loss 0.4654 | Training F1 0.7953 | Validation Accuracy 0.8061 | Validation F1 0.8271\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0311 | Training Loss 0.4639 | Training F1 0.7949 | Validation Accuracy 0.8004 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0311 | Training Loss 0.4623 | Training F1 0.7981 | Validation Accuracy 0.8042 | Validation F1 0.8269\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0311 | Training Loss 0.4608 | Training F1 0.7958 | Validation Accuracy 0.8080 | Validation F1 0.8268\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0311 | Training Loss 0.4602 | Training F1 0.8038 | Validation Accuracy 0.8004 | Validation F1 0.8276\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0311 | Training Loss 0.4614 | Training F1 0.7933 | Validation Accuracy 0.8061 | Validation F1 0.8185\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0311 | Training Loss 0.4620 | Training F1 0.8061 | Validation Accuracy 0.8004 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0311 | Training Loss 0.4561 | Training F1 0.8068 | Validation Accuracy 0.8004 | Validation F1 0.8264\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0310 | Training Loss 0.4555 | Training F1 0.8011 | Validation Accuracy 0.8061 | Validation F1 0.8217\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0310 | Training Loss 0.4567 | Training F1 0.8076 | Validation Accuracy 0.8080 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0310 | Training Loss 0.4520 | Training F1 0.8141 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0310 | Training Loss 0.4532 | Training F1 0.8032 | Validation Accuracy 0.8156 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0310 | Training Loss 0.4508 | Training F1 0.8051 | Validation Accuracy 0.8118 | Validation F1 0.8296\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0310 | Training Loss 0.4491 | Training F1 0.8100 | Validation Accuracy 0.8023 | Validation F1 0.8295\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0310 | Training Loss 0.4493 | Training F1 0.8105 | Validation Accuracy 0.8061 | Validation F1 0.8271\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0310 | Training Loss 0.4465 | Training F1 0.8085 | Validation Accuracy 0.8156 | Validation F1 0.8325\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0310 | Training Loss 0.4463 | Training F1 0.8107 | Validation Accuracy 0.8023 | Validation F1 0.8289\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0310 | Training Loss 0.4456 | Training F1 0.8118 | Validation Accuracy 0.8042 | Validation F1 0.8275\n",
      "INFO:root:Epoch 00100 | Time(s) 0.0310 | Training Loss 0.4426 | Training F1 0.8115 | Validation Accuracy 0.8213 | Validation F1 0.8385\n",
      "INFO:root:Epoch 00101 | Time(s) 0.0310 | Training Loss 0.4427 | Training F1 0.8158 | Validation Accuracy 0.8023 | Validation F1 0.8284\n",
      "INFO:root:Epoch 00102 | Time(s) 0.0310 | Training Loss 0.4410 | Training F1 0.8163 | Validation Accuracy 0.8061 | Validation F1 0.8289\n",
      "INFO:root:Epoch 00103 | Time(s) 0.0310 | Training Loss 0.4388 | Training F1 0.8164 | Validation Accuracy 0.8194 | Validation F1 0.8354\n",
      "INFO:root:Epoch 00104 | Time(s) 0.0310 | Training Loss 0.4385 | Training F1 0.8142 | Validation Accuracy 0.8042 | Validation F1 0.8292\n",
      "INFO:root:Epoch 00105 | Time(s) 0.0310 | Training Loss 0.4371 | Training F1 0.8194 | Validation Accuracy 0.8099 | Validation F1 0.8311\n",
      "INFO:root:Epoch 00106 | Time(s) 0.0310 | Training Loss 0.4355 | Training F1 0.8179 | Validation Accuracy 0.8080 | Validation F1 0.8279\n",
      "INFO:root:Epoch 00107 | Time(s) 0.0310 | Training Loss 0.4344 | Training F1 0.8132 | Validation Accuracy 0.8080 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00108 | Time(s) 0.0310 | Training Loss 0.4338 | Training F1 0.8157 | Validation Accuracy 0.8061 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00109 | Time(s) 0.0310 | Training Loss 0.4321 | Training F1 0.8221 | Validation Accuracy 0.8061 | Validation F1 0.8277\n",
      "INFO:root:Epoch 00110 | Time(s) 0.0310 | Training Loss 0.4311 | Training F1 0.8179 | Validation Accuracy 0.8099 | Validation F1 0.8328\n",
      "INFO:root:Epoch 00111 | Time(s) 0.0310 | Training Loss 0.4300 | Training F1 0.8149 | Validation Accuracy 0.8080 | Validation F1 0.8262\n",
      "INFO:root:Epoch 00112 | Time(s) 0.0310 | Training Loss 0.4295 | Training F1 0.8182 | Validation Accuracy 0.8099 | Validation F1 0.8333\n",
      "INFO:root:Epoch 00113 | Time(s) 0.0310 | Training Loss 0.4282 | Training F1 0.8162 | Validation Accuracy 0.8118 | Validation F1 0.8296\n",
      "INFO:root:Epoch 00114 | Time(s) 0.0310 | Training Loss 0.4278 | Training F1 0.8179 | Validation Accuracy 0.8023 | Validation F1 0.8255\n",
      "INFO:root:Epoch 00115 | Time(s) 0.0310 | Training Loss 0.4256 | Training F1 0.8148 | Validation Accuracy 0.8137 | Validation F1 0.8328\n",
      "INFO:root:Epoch 00116 | Time(s) 0.0310 | Training Loss 0.4245 | Training F1 0.8183 | Validation Accuracy 0.8118 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00117 | Time(s) 0.0310 | Training Loss 0.4231 | Training F1 0.8175 | Validation Accuracy 0.8137 | Validation F1 0.8328\n",
      "INFO:root:Epoch 00118 | Time(s) 0.0310 | Training Loss 0.4220 | Training F1 0.8194 | Validation Accuracy 0.8175 | Validation F1 0.8356\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Finished Model training\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model\n",
      "INFO:gcn_local.train_dgl_pytorch_entry_point_local:Saving model predictions for test data\n"
     ]
    }
   ],
   "source": [
    "gcn_results_all = pd.DataFrame(columns = [\"F1 Score\", \"ROC AUC\",\"Accuracy\",\"MCC\",\"Balanced Accuracy\",\"Precision\",\"Recall\"])\n",
    "\n",
    "for j in range(10):\n",
    "    \n",
    "    entry_train(\n",
    "        training_dir = \"gcn_processed_data\",\n",
    "        model_dir = \"gcn_results\",\n",
    "        output_dir = \"gcn_results\",\n",
    "        features_train = \"features_train.csv\",\n",
    "        features_test = \"features_test.csv\",\n",
    "        target_column = target_column,\n",
    "        source_destination_node_index = \"src_dst_df.csv\",\n",
    "        n_hidden = hyperparameters[\"n_hidden\"],\n",
    "        n_layers = hyperparameters[\"n_layers\"],\n",
    "        dropout = hyperparameters[\"dropout\"],\n",
    "        weight_decay = hyperparameters[\"weight_decay\"],\n",
    "        n_epochs = hyperparameters[\"n_epochs\"],\n",
    "        lr = hyperparameters[\"lr\"],\n",
    "        aggregator_type = hyperparameters[\"aggregator_type\"],\n",
    "        predictions_file_name = \"predictions.csv\"\n",
    "    )\n",
    "    \n",
    "    gcn_output = pd.read_csv(os.path.join(\"gcn_results\", \"predictions.csv\"))\n",
    "    \n",
    "    y_true, gcn_y_pred, gcn_y_pred_prob_class_1 = gcn_output[\"target\"], gcn_output[\"pred\"], gcn_output[\"pred_proba_class_1\"]\n",
    "    metrics_gcn_results = classification_report(y_true, gcn_y_pred, zero_division=1, output_dict=True)\n",
    "\n",
    "    gcn_results = pd.DataFrame(\n",
    "        {\n",
    "            \"F1 Score\": metrics_gcn_results[\"1\"][\"f1-score\"],\n",
    "            \"ROC AUC\": roc_auc_score(y_true, gcn_y_pred_prob_class_1),\n",
    "            \"Accuracy\": metrics_gcn_results[\"accuracy\"],\n",
    "            \"MCC\": matthews_corrcoef(y_true, gcn_y_pred),\n",
    "            \"Balanced Accuracy\": balanced_accuracy_score(y_true, gcn_y_pred),\n",
    "            \"Precision\": metrics_gcn_results[\"1\"][\"precision\"],\n",
    "            \"Recall\": metrics_gcn_results[\"1\"][\"recall\"],        \n",
    "        },\n",
    "        index=[\"GCN No HPO\"]\n",
    "    )\n",
    "    \n",
    "    gcn_results_all = pd.concat([gcn_results_all, gcn_results])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28199c87",
   "metadata": {},
   "source": [
    "### Read the Prediction Output for the Test Data\n",
    "The current training process is undertaken in a transductive setting where the features of the test dataset, not including the target column, are used to construct the graph and thus the test data are included in the training process. At the end of training, the predictions on the test dataset are generated and saved in the **output_location** in the s3 bucket. \n",
    "\n",
    "**Important**: Note that the labels of the test dataset are not used for training, and our exercise is aimed at predicting these labels using node embeddings for the test dataset nodes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64348dc4",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "The various performance metrics are shown below. We can see that the deep learning model cannot deliver an optimal performance with only one configuration of hyperparameters. Hyperparameter optimization (HPO) is necessary to systematically find the optimal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1fd55921-c905-42c6-8bb0-275515356e39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>MCC</th>\n",
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       "      <td>0.853933</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.792023</td>\n",
       "      <td>0.858851</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.553824</td>\n",
       "      <td>0.777493</td>\n",
       "      <td>0.803468</td>\n",
       "      <td>0.780899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.811429</td>\n",
       "      <td>0.868491</td>\n",
       "      <td>0.799087</td>\n",
       "      <td>0.596982</td>\n",
       "      <td>0.799209</td>\n",
       "      <td>0.825581</td>\n",
       "      <td>0.797753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.828685</td>\n",
       "      <td>0.866027</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>0.605232</td>\n",
       "      <td>0.797006</td>\n",
       "      <td>0.785894</td>\n",
       "      <td>0.876404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.802168</td>\n",
       "      <td>0.858888</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.551190</td>\n",
       "      <td>0.772873</td>\n",
       "      <td>0.774869</td>\n",
       "      <td>0.831461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.797260</td>\n",
       "      <td>0.853298</td>\n",
       "      <td>0.774734</td>\n",
       "      <td>0.545020</td>\n",
       "      <td>0.770834</td>\n",
       "      <td>0.778075</td>\n",
       "      <td>0.817416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F1 Score   ROC AUC  Accuracy       MCC  Balanced Accuracy  Precision  \\\n",
       "0  0.808786  0.868892  0.774734  0.549294           0.765188   0.748804   \n",
       "1  0.799427  0.845832  0.786910  0.572873           0.787203   0.815789   \n",
       "2  0.807263  0.859495  0.789954  0.576553           0.787959   0.802778   \n",
       "3  0.792105  0.848688  0.759513  0.515342           0.751656   0.745050   \n",
       "4  0.802111  0.857330  0.771689  0.540192           0.764176   0.756219   \n",
       "5  0.792023  0.858851  0.777778  0.553824           0.777493   0.803468   \n",
       "6  0.811429  0.868491  0.799087  0.596982           0.799209   0.825581   \n",
       "7  0.828685  0.866027  0.803653  0.605232           0.797006   0.785894   \n",
       "8  0.802168  0.858888  0.777778  0.551190           0.772873   0.774869   \n",
       "9  0.797260  0.853298  0.774734  0.545020           0.770834   0.778075   \n",
       "\n",
       "     Recall  \n",
       "0  0.879213  \n",
       "1  0.783708  \n",
       "2  0.811798  \n",
       "3  0.845506  \n",
       "4  0.853933  \n",
       "5  0.780899  \n",
       "6  0.797753  \n",
       "7  0.876404  \n",
       "8  0.831461  \n",
       "9  0.817416  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcn_results_all.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58206883",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>AutoGluon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.804126</td>\n",
       "      <td>0.010833</td>\n",
       "      <td>0.719251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>0.858579</td>\n",
       "      <td>0.007823</td>\n",
       "      <td>0.749468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.781583</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.680365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.560650</td>\n",
       "      <td>0.027265</td>\n",
       "      <td>0.352409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.777360</td>\n",
       "      <td>0.015340</td>\n",
       "      <td>0.673490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.783653</td>\n",
       "      <td>0.028126</td>\n",
       "      <td>0.686224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.827809</td>\n",
       "      <td>0.035631</td>\n",
       "      <td>0.755618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Mean        SD  AutoGluon\n",
       "F1 Score           0.804126  0.010833   0.719251\n",
       "ROC AUC            0.858579  0.007823   0.749468\n",
       "Accuracy           0.781583  0.013332   0.680365\n",
       "MCC                0.560650  0.027265   0.352409\n",
       "Balanced Accuracy  0.777360  0.015340   0.673490\n",
       "Precision          0.783653  0.028126   0.686224\n",
       "Recall             0.827809  0.035631   0.755618"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mean = gcn_results_all.mean(axis=0)\n",
    "res_sd = gcn_results_all.std(axis=0)\n",
    "res = pd.concat([res_mean, res_sd], axis=1)\n",
    "res.columns = [\"Mean\", \"SD\"]\n",
    "res = pd.concat([res, ag_results.T], axis=1)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15e7299",
   "metadata": {},
   "source": [
    "The results for AutoGluon do not use the graph, only the tabular data. When we add in the graph data and use hyperparameter optimization (HPO), we get a material gain in performance, from F1 in the low 70s to high 70s/80s.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7765c9ed-db6f-477e-a945-7d3676fc0696",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Inductive Model\n",
    "\n",
    "We now modify the code to build the inductive version of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1079fb44-e96e-40da-a07b-bdea108c11ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from gcn_local.train_dgl_pytorch_entry_point_inductive import inductive_entry_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09ab15b5-bbd6-4891-954c-f05a8e130dc5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"n-hidden\": 32, #256\n",
    "    \"n-layers\": 2,\n",
    "    \"dropout\": 0.0,\n",
    "    \"weight-decay\": 5e-4,\n",
    "    \"n-epochs\": 100,\n",
    "    \"lr\": 0.01,\n",
    "    \"aggregator-type\": \"pool\",\n",
    "    \"target-column\": target_column\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8af26cea-76b0-4bfa-9ed7-5701fffb7b8b",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f68416200>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0275 | Training Loss 8.1399 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0277 | Training Loss 9.6570 | Training F1 0.0018 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0273 | Training Loss 5.7779 | Training F1 0.5626 | Validation Accuracy 0.5304 | Validation F1 0.5944\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0270 | Training Loss 1.3712 | Training F1 0.7006 | Validation Accuracy 0.5494 | Validation F1 0.7041\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0267 | Training Loss 2.9543 | Training F1 0.7028 | Validation Accuracy 0.5437 | Validation F1 0.7015\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0265 | Training Loss 2.8310 | Training F1 0.6949 | Validation Accuracy 0.5551 | Validation F1 0.7000\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0265 | Training Loss 1.5026 | Training F1 0.2035 | Validation Accuracy 0.4373 | Validation F1 0.1957\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0264 | Training Loss 1.3106 | Training F1 0.0656 | Validation Accuracy 0.4468 | Validation F1 0.0583\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0263 | Training Loss 1.7967 | Training F1 0.2003 | Validation Accuracy 0.4734 | Validation F1 0.2063\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0262 | Training Loss 1.2320 | Training F1 0.6619 | Validation Accuracy 0.6084 | Validation F1 0.6720\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0261 | Training Loss 0.7849 | Training F1 0.7117 | Validation Accuracy 0.5875 | Validation F1 0.7126\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0264 | Training Loss 1.0554 | Training F1 0.7106 | Validation Accuracy 0.5817 | Validation F1 0.7143\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0264 | Training Loss 1.1520 | Training F1 0.7223 | Validation Accuracy 0.5989 | Validation F1 0.7152\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0263 | Training Loss 0.9080 | Training F1 0.6996 | Validation Accuracy 0.6483 | Validation F1 0.7040\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0263 | Training Loss 0.6729 | Training F1 0.5175 | Validation Accuracy 0.5970 | Validation F1 0.5115\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0262 | Training Loss 0.7743 | Training F1 0.3992 | Validation Accuracy 0.5665 | Validation F1 0.4000\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0262 | Training Loss 0.8957 | Training F1 0.5131 | Validation Accuracy 0.5989 | Validation F1 0.4940\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0262 | Training Loss 0.7778 | Training F1 0.6930 | Validation Accuracy 0.6787 | Validation F1 0.6998\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0262 | Training Loss 0.6146 | Training F1 0.7467 | Validation Accuracy 0.6882 | Validation F1 0.7538\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0262 | Training Loss 0.6319 | Training F1 0.7476 | Validation Accuracy 0.6616 | Validation F1 0.7500\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0262 | Training Loss 0.7183 | Training F1 0.7477 | Validation Accuracy 0.6597 | Validation F1 0.7482\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0261 | Training Loss 0.7092 | Training F1 0.7585 | Validation Accuracy 0.6977 | Validation F1 0.7623\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0261 | Training Loss 0.6180 | Training F1 0.7464 | Validation Accuracy 0.7281 | Validation F1 0.7589\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0261 | Training Loss 0.5634 | Training F1 0.6851 | Validation Accuracy 0.6882 | Validation F1 0.6733\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0261 | Training Loss 0.6055 | Training F1 0.6551 | Validation Accuracy 0.6939 | Validation F1 0.6653\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0261 | Training Loss 0.6282 | Training F1 0.7044 | Validation Accuracy 0.7129 | Validation F1 0.7068\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0261 | Training Loss 0.5882 | Training F1 0.7493 | Validation Accuracy 0.7243 | Validation F1 0.7406\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0261 | Training Loss 0.5458 | Training F1 0.7690 | Validation Accuracy 0.7529 | Validation F1 0.7903\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0261 | Training Loss 0.5384 | Training F1 0.7741 | Validation Accuracy 0.7471 | Validation F1 0.7932\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0260 | Training Loss 0.5476 | Training F1 0.7782 | Validation Accuracy 0.7490 | Validation F1 0.7937\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0260 | Training Loss 0.5389 | Training F1 0.7732 | Validation Accuracy 0.7605 | Validation F1 0.7879\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0260 | Training Loss 0.5194 | Training F1 0.7578 | Validation Accuracy 0.7586 | Validation F1 0.7687\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0260 | Training Loss 0.5252 | Training F1 0.7640 | Validation Accuracy 0.7510 | Validation F1 0.7614\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0260 | Training Loss 0.5196 | Training F1 0.7766 | Validation Accuracy 0.7662 | Validation F1 0.7883\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0260 | Training Loss 0.5054 | Training F1 0.7915 | Validation Accuracy 0.7624 | Validation F1 0.7967\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0260 | Training Loss 0.5072 | Training F1 0.7895 | Validation Accuracy 0.7586 | Validation F1 0.7955\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0260 | Training Loss 0.5064 | Training F1 0.7879 | Validation Accuracy 0.7681 | Validation F1 0.7967\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0260 | Training Loss 0.4950 | Training F1 0.7830 | Validation Accuracy 0.7795 | Validation F1 0.7943\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0260 | Training Loss 0.4919 | Training F1 0.7742 | Validation Accuracy 0.7719 | Validation F1 0.7818\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0260 | Training Loss 0.4934 | Training F1 0.7866 | Validation Accuracy 0.7776 | Validation F1 0.7929\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0260 | Training Loss 0.4850 | Training F1 0.7940 | Validation Accuracy 0.7852 | Validation F1 0.8107\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0259 | Training Loss 0.4811 | Training F1 0.7982 | Validation Accuracy 0.7719 | Validation F1 0.8039\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0259 | Training Loss 0.4826 | Training F1 0.7966 | Validation Accuracy 0.7852 | Validation F1 0.8114\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0259 | Training Loss 0.4766 | Training F1 0.7917 | Validation Accuracy 0.7795 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0259 | Training Loss 0.4727 | Training F1 0.7924 | Validation Accuracy 0.7814 | Validation F1 0.7928\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0259 | Training Loss 0.4732 | Training F1 0.7913 | Validation Accuracy 0.7852 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0259 | Training Loss 0.4678 | Training F1 0.8010 | Validation Accuracy 0.7852 | Validation F1 0.8107\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0259 | Training Loss 0.4649 | Training F1 0.8042 | Validation Accuracy 0.7738 | Validation F1 0.8040\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0259 | Training Loss 0.4650 | Training F1 0.8044 | Validation Accuracy 0.7871 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0260 | Training Loss 0.4611 | Training F1 0.8019 | Validation Accuracy 0.7795 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0260 | Training Loss 0.4584 | Training F1 0.7973 | Validation Accuracy 0.7852 | Validation F1 0.8021\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0260 | Training Loss 0.4574 | Training F1 0.8068 | Validation Accuracy 0.7852 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0260 | Training Loss 0.4532 | Training F1 0.8134 | Validation Accuracy 0.7776 | Validation F1 0.8066\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0260 | Training Loss 0.4545 | Training F1 0.8087 | Validation Accuracy 0.7814 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0260 | Training Loss 0.4500 | Training F1 0.8037 | Validation Accuracy 0.7757 | Validation F1 0.7937\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0260 | Training Loss 0.4497 | Training F1 0.8114 | Validation Accuracy 0.7909 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0259 | Training Loss 0.4460 | Training F1 0.8151 | Validation Accuracy 0.7833 | Validation F1 0.8100\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0259 | Training Loss 0.4452 | Training F1 0.8123 | Validation Accuracy 0.7890 | Validation F1 0.8083\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0259 | Training Loss 0.4421 | Training F1 0.8114 | Validation Accuracy 0.7814 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0259 | Training Loss 0.4411 | Training F1 0.8158 | Validation Accuracy 0.7852 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0259 | Training Loss 0.4384 | Training F1 0.8204 | Validation Accuracy 0.7985 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0259 | Training Loss 0.4375 | Training F1 0.8201 | Validation Accuracy 0.7852 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0259 | Training Loss 0.4349 | Training F1 0.8157 | Validation Accuracy 0.7909 | Validation F1 0.8084\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0259 | Training Loss 0.4338 | Training F1 0.8179 | Validation Accuracy 0.7928 | Validation F1 0.8111\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0259 | Training Loss 0.4321 | Training F1 0.8243 | Validation Accuracy 0.7890 | Validation F1 0.8115\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0259 | Training Loss 0.4308 | Training F1 0.8218 | Validation Accuracy 0.7871 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0259 | Training Loss 0.4291 | Training F1 0.8216 | Validation Accuracy 0.7947 | Validation F1 0.8125\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0259 | Training Loss 0.4277 | Training F1 0.8220 | Validation Accuracy 0.8004 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0259 | Training Loss 0.4262 | Training F1 0.8236 | Validation Accuracy 0.7890 | Validation F1 0.8109\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0259 | Training Loss 0.4247 | Training F1 0.8260 | Validation Accuracy 0.7928 | Validation F1 0.8137\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0259 | Training Loss 0.4232 | Training F1 0.8221 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0259 | Training Loss 0.4226 | Training F1 0.8274 | Validation Accuracy 0.7966 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0259 | Training Loss 0.4222 | Training F1 0.8257 | Validation Accuracy 0.8004 | Validation F1 0.8193\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0259 | Training Loss 0.4190 | Training F1 0.8263 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0259 | Training Loss 0.4191 | Training F1 0.8282 | Validation Accuracy 0.8042 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0259 | Training Loss 0.4163 | Training F1 0.8313 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0259 | Training Loss 0.4160 | Training F1 0.8297 | Validation Accuracy 0.8061 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0259 | Training Loss 0.4136 | Training F1 0.8296 | Validation Accuracy 0.8042 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0259 | Training Loss 0.4132 | Training F1 0.8321 | Validation Accuracy 0.7985 | Validation F1 0.8172\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0259 | Training Loss 0.4109 | Training F1 0.8333 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0259 | Training Loss 0.4104 | Training F1 0.8327 | Validation Accuracy 0.8004 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0259 | Training Loss 0.4085 | Training F1 0.8330 | Validation Accuracy 0.8004 | Validation F1 0.8161\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0259 | Training Loss 0.4074 | Training F1 0.8351 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0259 | Training Loss 0.4061 | Training F1 0.8357 | Validation Accuracy 0.7966 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0259 | Training Loss 0.4042 | Training F1 0.8335 | Validation Accuracy 0.8042 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0259 | Training Loss 0.4034 | Training F1 0.8380 | Validation Accuracy 0.8004 | Validation F1 0.8217\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0259 | Training Loss 0.4020 | Training F1 0.8376 | Validation Accuracy 0.8042 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0259 | Training Loss 0.4003 | Training F1 0.8371 | Validation Accuracy 0.8023 | Validation F1 0.8175\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0259 | Training Loss 0.3994 | Training F1 0.8398 | Validation Accuracy 0.8061 | Validation F1 0.8253\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0259 | Training Loss 0.3983 | Training F1 0.8411 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0259 | Training Loss 0.3969 | Training F1 0.8405 | Validation Accuracy 0.8042 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0259 | Training Loss 0.3954 | Training F1 0.8435 | Validation Accuracy 0.8061 | Validation F1 0.8247\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0259 | Training Loss 0.3944 | Training F1 0.8418 | Validation Accuracy 0.8099 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0259 | Training Loss 0.3935 | Training F1 0.8438 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0259 | Training Loss 0.3925 | Training F1 0.8397 | Validation Accuracy 0.8099 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0259 | Training Loss 0.3915 | Training F1 0.8454 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0259 | Training Loss 0.3902 | Training F1 0.8415 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0259 | Training Loss 0.3889 | Training F1 0.8462 | Validation Accuracy 0.8004 | Validation F1 0.8205\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0259 | Training Loss 0.3875 | Training F1 0.8456 | Validation Accuracy 0.8004 | Validation F1 0.8168\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0259 | Training Loss 0.3862 | Training F1 0.8449 | Validation Accuracy 0.8042 | Validation F1 0.8227\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f68416f20>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0272 | Training Loss 29.3870 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0268 | Training Loss 9.5739 | Training F1 0.6957 | Validation Accuracy 0.5361 | Validation F1 0.6973\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0266 | Training Loss 2.8766 | Training F1 0.7017 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0265 | Training Loss 4.6837 | Training F1 0.7017 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0264 | Training Loss 3.3857 | Training F1 0.6501 | Validation Accuracy 0.4810 | Validation F1 0.6345\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0263 | Training Loss 1.4257 | Training F1 0.0625 | Validation Accuracy 0.4373 | Validation F1 0.0692\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0262 | Training Loss 1.9109 | Training F1 0.0189 | Validation Accuracy 0.4506 | Validation F1 0.0069\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0262 | Training Loss 2.1215 | Training F1 0.1568 | Validation Accuracy 0.4468 | Validation F1 0.1516\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0261 | Training Loss 1.2664 | Training F1 0.6651 | Validation Accuracy 0.5399 | Validation F1 0.6703\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0261 | Training Loss 0.9332 | Training F1 0.7011 | Validation Accuracy 0.5399 | Validation F1 0.7005\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0260 | Training Loss 1.3630 | Training F1 0.7020 | Validation Accuracy 0.5475 | Validation F1 0.7040\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0260 | Training Loss 1.2192 | Training F1 0.6861 | Validation Accuracy 0.5798 | Validation F1 0.6985\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0260 | Training Loss 0.8063 | Training F1 0.4169 | Validation Accuracy 0.5247 | Validation F1 0.4104\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0259 | Training Loss 0.8353 | Training F1 0.1843 | Validation Accuracy 0.4791 | Validation F1 0.1697\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0259 | Training Loss 1.0391 | Training F1 0.2331 | Validation Accuracy 0.4905 | Validation F1 0.2299\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0259 | Training Loss 0.9573 | Training F1 0.5141 | Validation Accuracy 0.5875 | Validation F1 0.5393\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0259 | Training Loss 0.7580 | Training F1 0.6715 | Validation Accuracy 0.6103 | Validation F1 0.7007\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0259 | Training Loss 0.7205 | Training F1 0.7020 | Validation Accuracy 0.5894 | Validation F1 0.7165\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0259 | Training Loss 0.8148 | Training F1 0.7039 | Validation Accuracy 0.5856 | Validation F1 0.7183\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0259 | Training Loss 0.8462 | Training F1 0.7046 | Validation Accuracy 0.6065 | Validation F1 0.7236\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0261 | Training Loss 0.7628 | Training F1 0.6894 | Validation Accuracy 0.6426 | Validation F1 0.7202\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0261 | Training Loss 0.6679 | Training F1 0.6023 | Validation Accuracy 0.6274 | Validation F1 0.6231\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0261 | Training Loss 0.6693 | Training F1 0.5058 | Validation Accuracy 0.6103 | Validation F1 0.5434\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0261 | Training Loss 0.7211 | Training F1 0.5213 | Validation Accuracy 0.6236 | Validation F1 0.5639\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0261 | Training Loss 0.7127 | Training F1 0.6274 | Validation Accuracy 0.6654 | Validation F1 0.6615\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0260 | Training Loss 0.6441 | Training F1 0.7071 | Validation Accuracy 0.6825 | Validation F1 0.7345\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0260 | Training Loss 0.6109 | Training F1 0.7424 | Validation Accuracy 0.6768 | Validation F1 0.7536\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0260 | Training Loss 0.6436 | Training F1 0.7427 | Validation Accuracy 0.6711 | Validation F1 0.7560\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0260 | Training Loss 0.6655 | Training F1 0.7434 | Validation Accuracy 0.6806 | Validation F1 0.7558\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0260 | Training Loss 0.6327 | Training F1 0.7193 | Validation Accuracy 0.7053 | Validation F1 0.7551\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0260 | Training Loss 0.5927 | Training F1 0.6817 | Validation Accuracy 0.6996 | Validation F1 0.7138\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0259 | Training Loss 0.5986 | Training F1 0.6377 | Validation Accuracy 0.6882 | Validation F1 0.6772\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0259 | Training Loss 0.6202 | Training F1 0.6583 | Validation Accuracy 0.6920 | Validation F1 0.6873\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0259 | Training Loss 0.6071 | Training F1 0.7095 | Validation Accuracy 0.7148 | Validation F1 0.7396\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0259 | Training Loss 0.5761 | Training F1 0.7415 | Validation Accuracy 0.7243 | Validation F1 0.7759\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0259 | Training Loss 0.5770 | Training F1 0.7498 | Validation Accuracy 0.7053 | Validation F1 0.7704\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0259 | Training Loss 0.5896 | Training F1 0.7524 | Validation Accuracy 0.7205 | Validation F1 0.7769\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0259 | Training Loss 0.5784 | Training F1 0.7454 | Validation Accuracy 0.7319 | Validation F1 0.7737\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0259 | Training Loss 0.5608 | Training F1 0.7221 | Validation Accuracy 0.7338 | Validation F1 0.7578\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0259 | Training Loss 0.5600 | Training F1 0.7118 | Validation Accuracy 0.7319 | Validation F1 0.7422\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0259 | Training Loss 0.5672 | Training F1 0.7179 | Validation Accuracy 0.7338 | Validation F1 0.7464\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0259 | Training Loss 0.5614 | Training F1 0.7340 | Validation Accuracy 0.7452 | Validation F1 0.7721\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0259 | Training Loss 0.5492 | Training F1 0.7525 | Validation Accuracy 0.7510 | Validation F1 0.7924\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0259 | Training Loss 0.5476 | Training F1 0.7592 | Validation Accuracy 0.7510 | Validation F1 0.7924\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0259 | Training Loss 0.5452 | Training F1 0.7479 | Validation Accuracy 0.7490 | Validation F1 0.7807\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0258 | Training Loss 0.5390 | Training F1 0.7423 | Validation Accuracy 0.7490 | Validation F1 0.7708\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0258 | Training Loss 0.5383 | Training F1 0.7636 | Validation Accuracy 0.7586 | Validation F1 0.7961\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0259 | Training Loss 0.5351 | Training F1 0.7633 | Validation Accuracy 0.7567 | Validation F1 0.7908\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0259 | Training Loss 0.5298 | Training F1 0.7543 | Validation Accuracy 0.7548 | Validation F1 0.7802\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0259 | Training Loss 0.5279 | Training F1 0.7573 | Validation Accuracy 0.7548 | Validation F1 0.7825\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0259 | Training Loss 0.5240 | Training F1 0.7687 | Validation Accuracy 0.7624 | Validation F1 0.7974\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0259 | Training Loss 0.5216 | Training F1 0.7705 | Validation Accuracy 0.7624 | Validation F1 0.7947\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0258 | Training Loss 0.5177 | Training F1 0.7633 | Validation Accuracy 0.7567 | Validation F1 0.7816\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0258 | Training Loss 0.5159 | Training F1 0.7744 | Validation Accuracy 0.7662 | Validation F1 0.7974\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0258 | Training Loss 0.5110 | Training F1 0.7761 | Validation Accuracy 0.7700 | Validation F1 0.8033\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0258 | Training Loss 0.5081 | Training F1 0.7652 | Validation Accuracy 0.7719 | Validation F1 0.7952\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0258 | Training Loss 0.5059 | Training F1 0.7846 | Validation Accuracy 0.7662 | Validation F1 0.8013\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0258 | Training Loss 0.5024 | Training F1 0.7799 | Validation Accuracy 0.7776 | Validation F1 0.8072\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0262 | Training Loss 0.4972 | Training F1 0.7706 | Validation Accuracy 0.7700 | Validation F1 0.7939\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0262 | Training Loss 0.4964 | Training F1 0.7890 | Validation Accuracy 0.7738 | Validation F1 0.8065\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0262 | Training Loss 0.4912 | Training F1 0.7895 | Validation Accuracy 0.7757 | Validation F1 0.8072\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0262 | Training Loss 0.4878 | Training F1 0.7802 | Validation Accuracy 0.7871 | Validation F1 0.8089\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0262 | Training Loss 0.4854 | Training F1 0.7938 | Validation Accuracy 0.7776 | Validation F1 0.8072\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0261 | Training Loss 0.4802 | Training F1 0.7968 | Validation Accuracy 0.7776 | Validation F1 0.8079\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0261 | Training Loss 0.4773 | Training F1 0.7913 | Validation Accuracy 0.7833 | Validation F1 0.8068\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0261 | Training Loss 0.4747 | Training F1 0.8009 | Validation Accuracy 0.7871 | Validation F1 0.8146\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0261 | Training Loss 0.4703 | Training F1 0.8036 | Validation Accuracy 0.7871 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0261 | Training Loss 0.4667 | Training F1 0.8014 | Validation Accuracy 0.7852 | Validation F1 0.8081\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0261 | Training Loss 0.4642 | Training F1 0.8105 | Validation Accuracy 0.7662 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0261 | Training Loss 0.4653 | Training F1 0.7762 | Validation Accuracy 0.7814 | Validation F1 0.7905\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0261 | Training Loss 0.4756 | Training F1 0.8050 | Validation Accuracy 0.7490 | Validation F1 0.7975\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0261 | Training Loss 0.4771 | Training F1 0.8147 | Validation Accuracy 0.7928 | Validation F1 0.8174\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0261 | Training Loss 0.4516 | Training F1 0.7788 | Validation Accuracy 0.7833 | Validation F1 0.7889\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0261 | Training Loss 0.4704 | Training F1 0.8111 | Validation Accuracy 0.7776 | Validation F1 0.8104\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0261 | Training Loss 0.4518 | Training F1 0.8178 | Validation Accuracy 0.7643 | Validation F1 0.8032\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0261 | Training Loss 0.4552 | Training F1 0.7991 | Validation Accuracy 0.7947 | Validation F1 0.8112\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0261 | Training Loss 0.4474 | Training F1 0.7985 | Validation Accuracy 0.7947 | Validation F1 0.8099\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0262 | Training Loss 0.4482 | Training F1 0.8191 | Validation Accuracy 0.7814 | Validation F1 0.8130\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0262 | Training Loss 0.4441 | Training F1 0.8199 | Validation Accuracy 0.7814 | Validation F1 0.8136\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0263 | Training Loss 0.4427 | Training F1 0.8087 | Validation Accuracy 0.7966 | Validation F1 0.8139\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0264 | Training Loss 0.4398 | Training F1 0.8147 | Validation Accuracy 0.7985 | Validation F1 0.8166\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0264 | Training Loss 0.4363 | Training F1 0.8241 | Validation Accuracy 0.7795 | Validation F1 0.8123\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0265 | Training Loss 0.4364 | Training F1 0.8265 | Validation Accuracy 0.7890 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0269 | Training Loss 0.4307 | Training F1 0.8159 | Validation Accuracy 0.8042 | Validation F1 0.8209\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0270 | Training Loss 0.4330 | Training F1 0.8267 | Validation Accuracy 0.8080 | Validation F1 0.8297\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0270 | Training Loss 0.4257 | Training F1 0.8295 | Validation Accuracy 0.7814 | Validation F1 0.8136\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0270 | Training Loss 0.4283 | Training F1 0.8257 | Validation Accuracy 0.8061 | Validation F1 0.8289\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0270 | Training Loss 0.4217 | Training F1 0.8215 | Validation Accuracy 0.8042 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0270 | Training Loss 0.4230 | Training F1 0.8335 | Validation Accuracy 0.7928 | Validation F1 0.8216\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0270 | Training Loss 0.4193 | Training F1 0.8326 | Validation Accuracy 0.7966 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0270 | Training Loss 0.4172 | Training F1 0.8236 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0270 | Training Loss 0.4171 | Training F1 0.8320 | Validation Accuracy 0.7985 | Validation F1 0.8233\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0271 | Training Loss 0.4122 | Training F1 0.8326 | Validation Accuracy 0.7966 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0271 | Training Loss 0.4127 | Training F1 0.8318 | Validation Accuracy 0.8118 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0271 | Training Loss 0.4097 | Training F1 0.8365 | Validation Accuracy 0.8061 | Validation F1 0.8277\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0271 | Training Loss 0.4070 | Training F1 0.8335 | Validation Accuracy 0.8023 | Validation F1 0.8284\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0271 | Training Loss 0.4068 | Training F1 0.8341 | Validation Accuracy 0.8080 | Validation F1 0.8285\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0271 | Training Loss 0.4035 | Training F1 0.8346 | Validation Accuracy 0.8042 | Validation F1 0.8263\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0271 | Training Loss 0.4012 | Training F1 0.8369 | Validation Accuracy 0.8023 | Validation F1 0.8284\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0271 | Training Loss 0.4008 | Training F1 0.8347 | Validation Accuracy 0.8061 | Validation F1 0.8265\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f68416f20>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0271 | Training Loss 5.2675 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0265 | Training Loss 8.0650 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0263 | Training Loss 5.5212 | Training F1 0.4313 | Validation Accuracy 0.5894 | Validation F1 0.4953\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0266 | Training Loss 1.0351 | Training F1 0.7028 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0264 | Training Loss 2.7913 | Training F1 0.7025 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0262 | Training Loss 3.7402 | Training F1 0.7025 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0261 | Training Loss 3.3057 | Training F1 0.7030 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0260 | Training Loss 2.1846 | Training F1 0.7185 | Validation Accuracy 0.6141 | Validation F1 0.7238\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0260 | Training Loss 0.9712 | Training F1 0.4634 | Validation Accuracy 0.5970 | Validation F1 0.4952\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0259 | Training Loss 0.8841 | Training F1 0.0753 | Validation Accuracy 0.4677 | Validation F1 0.0789\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0259 | Training Loss 1.5723 | Training F1 0.0478 | Validation Accuracy 0.4582 | Validation F1 0.0273\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0258 | Training Loss 1.6485 | Training F1 0.1627 | Validation Accuracy 0.5019 | Validation F1 0.2202\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0258 | Training Loss 1.2075 | Training F1 0.5833 | Validation Accuracy 0.6483 | Validation F1 0.6072\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0258 | Training Loss 0.7323 | Training F1 0.7341 | Validation Accuracy 0.6882 | Validation F1 0.7461\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0258 | Training Loss 0.6694 | Training F1 0.7297 | Validation Accuracy 0.6388 | Validation F1 0.7432\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0258 | Training Loss 0.8621 | Training F1 0.7273 | Validation Accuracy 0.6179 | Validation F1 0.7359\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0257 | Training Loss 0.9669 | Training F1 0.7262 | Validation Accuracy 0.6179 | Validation F1 0.7345\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0257 | Training Loss 0.9338 | Training F1 0.7302 | Validation Accuracy 0.6407 | Validation F1 0.7436\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0257 | Training Loss 0.8162 | Training F1 0.7438 | Validation Accuracy 0.6711 | Validation F1 0.7525\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0257 | Training Loss 0.6845 | Training F1 0.7297 | Validation Accuracy 0.6977 | Validation F1 0.7456\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0257 | Training Loss 0.6041 | Training F1 0.6815 | Validation Accuracy 0.7072 | Validation F1 0.7127\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0257 | Training Loss 0.6137 | Training F1 0.6321 | Validation Accuracy 0.7072 | Validation F1 0.6870\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0257 | Training Loss 0.6422 | Training F1 0.6140 | Validation Accuracy 0.7129 | Validation F1 0.6874\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0257 | Training Loss 0.6432 | Training F1 0.6411 | Validation Accuracy 0.7243 | Validation F1 0.7082\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0259 | Training Loss 0.6217 | Training F1 0.6756 | Validation Accuracy 0.7338 | Validation F1 0.7287\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0259 | Training Loss 0.5937 | Training F1 0.7040 | Validation Accuracy 0.7338 | Validation F1 0.7445\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0259 | Training Loss 0.5702 | Training F1 0.7315 | Validation Accuracy 0.7357 | Validation F1 0.7624\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0259 | Training Loss 0.5584 | Training F1 0.7504 | Validation Accuracy 0.7167 | Validation F1 0.7577\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0259 | Training Loss 0.5566 | Training F1 0.7574 | Validation Accuracy 0.7129 | Validation F1 0.7607\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0259 | Training Loss 0.5580 | Training F1 0.7610 | Validation Accuracy 0.7262 | Validation F1 0.7750\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0259 | Training Loss 0.5560 | Training F1 0.7593 | Validation Accuracy 0.7281 | Validation F1 0.7748\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0258 | Training Loss 0.5479 | Training F1 0.7637 | Validation Accuracy 0.7338 | Validation F1 0.7720\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0258 | Training Loss 0.5356 | Training F1 0.7589 | Validation Accuracy 0.7510 | Validation F1 0.7737\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0258 | Training Loss 0.5289 | Training F1 0.7531 | Validation Accuracy 0.7662 | Validation F1 0.7823\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0258 | Training Loss 0.5268 | Training F1 0.7592 | Validation Accuracy 0.7662 | Validation F1 0.7838\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0258 | Training Loss 0.5202 | Training F1 0.7725 | Validation Accuracy 0.7719 | Validation F1 0.7931\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0258 | Training Loss 0.5134 | Training F1 0.7796 | Validation Accuracy 0.7776 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0258 | Training Loss 0.5084 | Training F1 0.7813 | Validation Accuracy 0.7814 | Validation F1 0.8067\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0260 | Training Loss 0.5040 | Training F1 0.7857 | Validation Accuracy 0.7871 | Validation F1 0.8108\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0260 | Training Loss 0.5001 | Training F1 0.7894 | Validation Accuracy 0.7852 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0260 | Training Loss 0.4961 | Training F1 0.7916 | Validation Accuracy 0.7928 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0259 | Training Loss 0.4921 | Training F1 0.7864 | Validation Accuracy 0.7966 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0259 | Training Loss 0.4887 | Training F1 0.7874 | Validation Accuracy 0.7966 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0259 | Training Loss 0.4852 | Training F1 0.7932 | Validation Accuracy 0.7966 | Validation F1 0.8165\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0259 | Training Loss 0.4815 | Training F1 0.7961 | Validation Accuracy 0.7928 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0259 | Training Loss 0.4784 | Training F1 0.7991 | Validation Accuracy 0.7966 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0259 | Training Loss 0.4752 | Training F1 0.7974 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0259 | Training Loss 0.4718 | Training F1 0.7958 | Validation Accuracy 0.7985 | Validation F1 0.8172\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0259 | Training Loss 0.4691 | Training F1 0.7993 | Validation Accuracy 0.7985 | Validation F1 0.8185\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0259 | Training Loss 0.4657 | Training F1 0.8017 | Validation Accuracy 0.7985 | Validation F1 0.8203\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0259 | Training Loss 0.4626 | Training F1 0.8002 | Validation Accuracy 0.8004 | Validation F1 0.8211\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0259 | Training Loss 0.4589 | Training F1 0.7967 | Validation Accuracy 0.8061 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0258 | Training Loss 0.4560 | Training F1 0.8055 | Validation Accuracy 0.7966 | Validation F1 0.8196\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0258 | Training Loss 0.4526 | Training F1 0.8050 | Validation Accuracy 0.7966 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0258 | Training Loss 0.4489 | Training F1 0.8023 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0258 | Training Loss 0.4466 | Training F1 0.8109 | Validation Accuracy 0.7966 | Validation F1 0.8202\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0258 | Training Loss 0.4436 | Training F1 0.8111 | Validation Accuracy 0.8080 | Validation F1 0.8268\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0258 | Training Loss 0.4396 | Training F1 0.8113 | Validation Accuracy 0.8042 | Validation F1 0.8209\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0258 | Training Loss 0.4377 | Training F1 0.8179 | Validation Accuracy 0.8118 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0258 | Training Loss 0.4355 | Training F1 0.8146 | Validation Accuracy 0.8099 | Validation F1 0.8276\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0258 | Training Loss 0.4318 | Training F1 0.8137 | Validation Accuracy 0.8042 | Validation F1 0.8196\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0258 | Training Loss 0.4304 | Training F1 0.8218 | Validation Accuracy 0.8137 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0260 | Training Loss 0.4292 | Training F1 0.8157 | Validation Accuracy 0.8118 | Validation F1 0.8278\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0259 | Training Loss 0.4248 | Training F1 0.8162 | Validation Accuracy 0.8080 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0259 | Training Loss 0.4230 | Training F1 0.8257 | Validation Accuracy 0.8118 | Validation F1 0.8336\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0259 | Training Loss 0.4220 | Training F1 0.8208 | Validation Accuracy 0.8099 | Validation F1 0.8246\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0259 | Training Loss 0.4176 | Training F1 0.8201 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0259 | Training Loss 0.4158 | Training F1 0.8283 | Validation Accuracy 0.8156 | Validation F1 0.8375\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0259 | Training Loss 0.4156 | Training F1 0.8214 | Validation Accuracy 0.8061 | Validation F1 0.8191\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0259 | Training Loss 0.4117 | Training F1 0.8263 | Validation Accuracy 0.8118 | Validation F1 0.8266\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0259 | Training Loss 0.4085 | Training F1 0.8303 | Validation Accuracy 0.8213 | Validation F1 0.8412\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0259 | Training Loss 0.4082 | Training F1 0.8239 | Validation Accuracy 0.8080 | Validation F1 0.8212\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0259 | Training Loss 0.4056 | Training F1 0.8338 | Validation Accuracy 0.8175 | Validation F1 0.8333\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0259 | Training Loss 0.4022 | Training F1 0.8302 | Validation Accuracy 0.8175 | Validation F1 0.8356\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0259 | Training Loss 0.4006 | Training F1 0.8298 | Validation Accuracy 0.8042 | Validation F1 0.8177\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0259 | Training Loss 0.3995 | Training F1 0.8325 | Validation Accuracy 0.8137 | Validation F1 0.8350\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0259 | Training Loss 0.3976 | Training F1 0.8351 | Validation Accuracy 0.8061 | Validation F1 0.8204\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0259 | Training Loss 0.3945 | Training F1 0.8353 | Validation Accuracy 0.8156 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0259 | Training Loss 0.3919 | Training F1 0.8349 | Validation Accuracy 0.8137 | Validation F1 0.8322\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0259 | Training Loss 0.3907 | Training F1 0.8362 | Validation Accuracy 0.8080 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0258 | Training Loss 0.3904 | Training F1 0.8402 | Validation Accuracy 0.8156 | Validation F1 0.8375\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0258 | Training Loss 0.3904 | Training F1 0.8351 | Validation Accuracy 0.8118 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0258 | Training Loss 0.3886 | Training F1 0.8399 | Validation Accuracy 0.8194 | Validation F1 0.8393\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0258 | Training Loss 0.3848 | Training F1 0.8433 | Validation Accuracy 0.8137 | Validation F1 0.8310\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0258 | Training Loss 0.3819 | Training F1 0.8395 | Validation Accuracy 0.8137 | Validation F1 0.8275\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0258 | Training Loss 0.3820 | Training F1 0.8420 | Validation Accuracy 0.8137 | Validation F1 0.8367\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0258 | Training Loss 0.3834 | Training F1 0.8395 | Validation Accuracy 0.8194 | Validation F1 0.8301\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0258 | Training Loss 0.3824 | Training F1 0.8441 | Validation Accuracy 0.8175 | Validation F1 0.8373\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0258 | Training Loss 0.3782 | Training F1 0.8478 | Validation Accuracy 0.8213 | Validation F1 0.8390\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0258 | Training Loss 0.3751 | Training F1 0.8426 | Validation Accuracy 0.8175 | Validation F1 0.8298\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0258 | Training Loss 0.3766 | Training F1 0.8504 | Validation Accuracy 0.8099 | Validation F1 0.8344\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0258 | Training Loss 0.3772 | Training F1 0.8435 | Validation Accuracy 0.8175 | Validation F1 0.8310\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0258 | Training Loss 0.3726 | Training F1 0.8491 | Validation Accuracy 0.8118 | Validation F1 0.8290\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0258 | Training Loss 0.3699 | Training F1 0.8503 | Validation Accuracy 0.8118 | Validation F1 0.8353\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0258 | Training Loss 0.3714 | Training F1 0.8427 | Validation Accuracy 0.8270 | Validation F1 0.8384\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0258 | Training Loss 0.3710 | Training F1 0.8532 | Validation Accuracy 0.8175 | Validation F1 0.8389\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0258 | Training Loss 0.3673 | Training F1 0.8529 | Validation Accuracy 0.8156 | Validation F1 0.8364\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0258 | Training Loss 0.3652 | Training F1 0.8468 | Validation Accuracy 0.8232 | Validation F1 0.8366\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0258 | Training Loss 0.3662 | Training F1 0.8545 | Validation Accuracy 0.8099 | Validation F1 0.8333\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0258 | Training Loss 0.3653 | Training F1 0.8529 | Validation Accuracy 0.8213 | Validation F1 0.8362\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7fa002a35510>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0273 | Training Loss 6.1723 | Training F1 0.7032 | Validation Accuracy 0.5456 | Validation F1 0.7038\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0268 | Training Loss 2.2265 | Training F1 0.7035 | Validation Accuracy 0.5437 | Validation F1 0.7022\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0266 | Training Loss 1.7471 | Training F1 0.4997 | Validation Accuracy 0.5361 | Validation F1 0.4786\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0264 | Training Loss 0.8222 | Training F1 0.0458 | Validation Accuracy 0.4582 | Validation F1 0.0594\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0263 | Training Loss 1.2739 | Training F1 0.5827 | Validation Accuracy 0.5932 | Validation F1 0.5615\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0262 | Training Loss 0.6957 | Training F1 0.7354 | Validation Accuracy 0.6236 | Validation F1 0.7324\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0262 | Training Loss 0.7627 | Training F1 0.7254 | Validation Accuracy 0.5837 | Validation F1 0.7181\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0261 | Training Loss 0.8920 | Training F1 0.7380 | Validation Accuracy 0.6255 | Validation F1 0.7356\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0260 | Training Loss 0.7359 | Training F1 0.7266 | Validation Accuracy 0.6768 | Validation F1 0.7293\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0260 | Training Loss 0.6003 | Training F1 0.5936 | Validation Accuracy 0.6407 | Validation F1 0.6038\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0259 | Training Loss 0.6631 | Training F1 0.5028 | Validation Accuracy 0.6122 | Validation F1 0.5024\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0259 | Training Loss 0.7282 | Training F1 0.5764 | Validation Accuracy 0.6464 | Validation F1 0.5867\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0259 | Training Loss 0.6705 | Training F1 0.6944 | Validation Accuracy 0.6768 | Validation F1 0.6975\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0259 | Training Loss 0.5795 | Training F1 0.7533 | Validation Accuracy 0.7167 | Validation F1 0.7697\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0258 | Training Loss 0.5666 | Training F1 0.7670 | Validation Accuracy 0.6882 | Validation F1 0.7637\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0258 | Training Loss 0.5976 | Training F1 0.7675 | Validation Accuracy 0.6901 | Validation F1 0.7661\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0260 | Training Loss 0.5992 | Training F1 0.7692 | Validation Accuracy 0.7110 | Validation F1 0.7751\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0260 | Training Loss 0.5627 | Training F1 0.7615 | Validation Accuracy 0.7205 | Validation F1 0.7633\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0259 | Training Loss 0.5297 | Training F1 0.7385 | Validation Accuracy 0.7529 | Validation F1 0.7679\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0259 | Training Loss 0.5382 | Training F1 0.7218 | Validation Accuracy 0.7510 | Validation F1 0.7514\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0259 | Training Loss 0.5548 | Training F1 0.7361 | Validation Accuracy 0.7586 | Validation F1 0.7617\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0259 | Training Loss 0.5445 | Training F1 0.7563 | Validation Accuracy 0.7681 | Validation F1 0.7837\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0259 | Training Loss 0.5188 | Training F1 0.7847 | Validation Accuracy 0.7376 | Validation F1 0.7760\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0259 | Training Loss 0.5050 | Training F1 0.7888 | Validation Accuracy 0.7395 | Validation F1 0.7863\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0259 | Training Loss 0.5085 | Training F1 0.7899 | Validation Accuracy 0.7357 | Validation F1 0.7865\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0259 | Training Loss 0.5107 | Training F1 0.7974 | Validation Accuracy 0.7357 | Validation F1 0.7845\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0259 | Training Loss 0.5008 | Training F1 0.7933 | Validation Accuracy 0.7433 | Validation F1 0.7791\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0259 | Training Loss 0.4891 | Training F1 0.7839 | Validation Accuracy 0.7681 | Validation F1 0.7882\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0259 | Training Loss 0.4881 | Training F1 0.7763 | Validation Accuracy 0.7776 | Validation F1 0.7944\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0259 | Training Loss 0.4882 | Training F1 0.7914 | Validation Accuracy 0.7700 | Validation F1 0.7888\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0259 | Training Loss 0.4798 | Training F1 0.8010 | Validation Accuracy 0.7643 | Validation F1 0.7926\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0259 | Training Loss 0.4709 | Training F1 0.8071 | Validation Accuracy 0.7471 | Validation F1 0.7886\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0260 | Training Loss 0.4696 | Training F1 0.8099 | Validation Accuracy 0.7567 | Validation F1 0.7981\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0261 | Training Loss 0.4692 | Training F1 0.8135 | Validation Accuracy 0.7548 | Validation F1 0.7943\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0261 | Training Loss 0.4637 | Training F1 0.8052 | Validation Accuracy 0.7605 | Validation F1 0.7928\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0261 | Training Loss 0.4568 | Training F1 0.8021 | Validation Accuracy 0.7738 | Validation F1 0.7966\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0261 | Training Loss 0.4547 | Training F1 0.8007 | Validation Accuracy 0.7738 | Validation F1 0.7945\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0260 | Training Loss 0.4534 | Training F1 0.8065 | Validation Accuracy 0.7681 | Validation F1 0.7911\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0260 | Training Loss 0.4480 | Training F1 0.8120 | Validation Accuracy 0.7700 | Validation F1 0.7987\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0260 | Training Loss 0.4431 | Training F1 0.8174 | Validation Accuracy 0.7605 | Validation F1 0.7961\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0260 | Training Loss 0.4415 | Training F1 0.8194 | Validation Accuracy 0.7586 | Validation F1 0.7948\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0260 | Training Loss 0.4386 | Training F1 0.8194 | Validation Accuracy 0.7719 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0260 | Training Loss 0.4333 | Training F1 0.8192 | Validation Accuracy 0.7833 | Validation F1 0.8081\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0260 | Training Loss 0.4306 | Training F1 0.8145 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0260 | Training Loss 0.4299 | Training F1 0.8181 | Validation Accuracy 0.7966 | Validation F1 0.8165\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0260 | Training Loss 0.4269 | Training F1 0.8232 | Validation Accuracy 0.7928 | Validation F1 0.8162\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0260 | Training Loss 0.4228 | Training F1 0.8235 | Validation Accuracy 0.7928 | Validation F1 0.8180\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0260 | Training Loss 0.4202 | Training F1 0.8257 | Validation Accuracy 0.7909 | Validation F1 0.8148\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0260 | Training Loss 0.4173 | Training F1 0.8209 | Validation Accuracy 0.7947 | Validation F1 0.8157\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0260 | Training Loss 0.4151 | Training F1 0.8307 | Validation Accuracy 0.7966 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0260 | Training Loss 0.4129 | Training F1 0.8306 | Validation Accuracy 0.7966 | Validation F1 0.8214\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0260 | Training Loss 0.4094 | Training F1 0.8309 | Validation Accuracy 0.8023 | Validation F1 0.8219\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0260 | Training Loss 0.4071 | Training F1 0.8358 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0260 | Training Loss 0.4039 | Training F1 0.8347 | Validation Accuracy 0.7890 | Validation F1 0.8153\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0260 | Training Loss 0.4020 | Training F1 0.8373 | Validation Accuracy 0.7985 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0260 | Training Loss 0.3990 | Training F1 0.8369 | Validation Accuracy 0.8023 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0260 | Training Loss 0.3966 | Training F1 0.8392 | Validation Accuracy 0.8023 | Validation F1 0.8231\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0260 | Training Loss 0.3939 | Training F1 0.8415 | Validation Accuracy 0.7947 | Validation F1 0.8194\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0264 | Training Loss 0.3918 | Training F1 0.8428 | Validation Accuracy 0.7947 | Validation F1 0.8194\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0264 | Training Loss 0.3896 | Training F1 0.8377 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0264 | Training Loss 0.3874 | Training F1 0.8396 | Validation Accuracy 0.8042 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0265 | Training Loss 0.3852 | Training F1 0.8403 | Validation Accuracy 0.7985 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0265 | Training Loss 0.3829 | Training F1 0.8415 | Validation Accuracy 0.8004 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0265 | Training Loss 0.3808 | Training F1 0.8393 | Validation Accuracy 0.7985 | Validation F1 0.8203\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0265 | Training Loss 0.3787 | Training F1 0.8408 | Validation Accuracy 0.7985 | Validation F1 0.8203\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0265 | Training Loss 0.3765 | Training F1 0.8457 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0265 | Training Loss 0.3745 | Training F1 0.8435 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0265 | Training Loss 0.3722 | Training F1 0.8435 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0265 | Training Loss 0.3703 | Training F1 0.8470 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0265 | Training Loss 0.3680 | Training F1 0.8476 | Validation Accuracy 0.8004 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0265 | Training Loss 0.3658 | Training F1 0.8463 | Validation Accuracy 0.8042 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0265 | Training Loss 0.3639 | Training F1 0.8503 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0265 | Training Loss 0.3618 | Training F1 0.8516 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0265 | Training Loss 0.3597 | Training F1 0.8510 | Validation Accuracy 0.8023 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0265 | Training Loss 0.3578 | Training F1 0.8570 | Validation Accuracy 0.8080 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0265 | Training Loss 0.3558 | Training F1 0.8547 | Validation Accuracy 0.8080 | Validation F1 0.8279\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0265 | Training Loss 0.3536 | Training F1 0.8586 | Validation Accuracy 0.8137 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0265 | Training Loss 0.3515 | Training F1 0.8573 | Validation Accuracy 0.8137 | Validation F1 0.8339\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0265 | Training Loss 0.3494 | Training F1 0.8548 | Validation Accuracy 0.8194 | Validation F1 0.8376\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0265 | Training Loss 0.3476 | Training F1 0.8616 | Validation Accuracy 0.8194 | Validation F1 0.8414\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0265 | Training Loss 0.3458 | Training F1 0.8539 | Validation Accuracy 0.8194 | Validation F1 0.8365\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0265 | Training Loss 0.3444 | Training F1 0.8614 | Validation Accuracy 0.8118 | Validation F1 0.8358\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0265 | Training Loss 0.3427 | Training F1 0.8588 | Validation Accuracy 0.8194 | Validation F1 0.8365\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0265 | Training Loss 0.3401 | Training F1 0.8595 | Validation Accuracy 0.8194 | Validation F1 0.8403\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0265 | Training Loss 0.3376 | Training F1 0.8606 | Validation Accuracy 0.8156 | Validation F1 0.8359\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0265 | Training Loss 0.3356 | Training F1 0.8598 | Validation Accuracy 0.8194 | Validation F1 0.8387\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0265 | Training Loss 0.3340 | Training F1 0.8621 | Validation Accuracy 0.8156 | Validation F1 0.8391\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0265 | Training Loss 0.3329 | Training F1 0.8592 | Validation Accuracy 0.8213 | Validation F1 0.8385\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0265 | Training Loss 0.3313 | Training F1 0.8657 | Validation Accuracy 0.8175 | Validation F1 0.8411\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0265 | Training Loss 0.3288 | Training F1 0.8676 | Validation Accuracy 0.8175 | Validation F1 0.8373\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0265 | Training Loss 0.3261 | Training F1 0.8693 | Validation Accuracy 0.8213 | Validation F1 0.8407\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0265 | Training Loss 0.3241 | Training F1 0.8693 | Validation Accuracy 0.8137 | Validation F1 0.8372\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0265 | Training Loss 0.3229 | Training F1 0.8660 | Validation Accuracy 0.8232 | Validation F1 0.8399\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0265 | Training Loss 0.3220 | Training F1 0.8729 | Validation Accuracy 0.8080 | Validation F1 0.8331\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0265 | Training Loss 0.3215 | Training F1 0.8687 | Validation Accuracy 0.8213 | Validation F1 0.8385\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0265 | Training Loss 0.3188 | Training F1 0.8752 | Validation Accuracy 0.8118 | Validation F1 0.8353\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0265 | Training Loss 0.3152 | Training F1 0.8762 | Validation Accuracy 0.8213 | Validation F1 0.8412\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0265 | Training Loss 0.3128 | Training F1 0.8757 | Validation Accuracy 0.8270 | Validation F1 0.8444\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0265 | Training Loss 0.3118 | Training F1 0.8767 | Validation Accuracy 0.8156 | Validation F1 0.8407\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0265 | Training Loss 0.3123 | Training F1 0.8698 | Validation Accuracy 0.8194 | Validation F1 0.8365\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f68416e40>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0284 | Training Loss 6.7295 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0278 | Training Loss 7.4161 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0326 | Training Loss 5.5449 | Training F1 0.7036 | Validation Accuracy 0.5589 | Validation F1 0.7107\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0311 | Training Loss 1.6305 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0301 | Training Loss 3.1154 | Training F1 0.0000 | Validation Accuracy 0.4563 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0295 | Training Loss 3.0528 | Training F1 0.1449 | Validation Accuracy 0.4639 | Validation F1 0.1455\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0292 | Training Loss 1.4764 | Training F1 0.6890 | Validation Accuracy 0.6160 | Validation F1 0.7187\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0288 | Training Loss 0.8629 | Training F1 0.7042 | Validation Accuracy 0.5532 | Validation F1 0.7081\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0285 | Training Loss 1.3913 | Training F1 0.7042 | Validation Accuracy 0.5494 | Validation F1 0.7063\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0282 | Training Loss 1.4435 | Training F1 0.7062 | Validation Accuracy 0.5760 | Validation F1 0.7152\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0281 | Training Loss 1.0770 | Training F1 0.6588 | Validation Accuracy 0.6236 | Validation F1 0.6897\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0279 | Training Loss 0.7471 | Training F1 0.3048 | Validation Accuracy 0.5190 | Validation F1 0.3253\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0278 | Training Loss 0.9710 | Training F1 0.1710 | Validation Accuracy 0.4962 | Validation F1 0.2183\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0278 | Training Loss 1.0782 | Training F1 0.3285 | Validation Accuracy 0.5304 | Validation F1 0.3584\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0277 | Training Loss 0.8855 | Training F1 0.6211 | Validation Accuracy 0.6236 | Validation F1 0.6320\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0276 | Training Loss 0.6970 | Training F1 0.7008 | Validation Accuracy 0.6426 | Validation F1 0.7202\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0275 | Training Loss 0.6989 | Training F1 0.7083 | Validation Accuracy 0.6274 | Validation F1 0.7315\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0274 | Training Loss 0.7555 | Training F1 0.7092 | Validation Accuracy 0.6160 | Validation F1 0.7307\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0274 | Training Loss 0.7674 | Training F1 0.7123 | Validation Accuracy 0.6293 | Validation F1 0.7347\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0273 | Training Loss 0.7303 | Training F1 0.7149 | Validation Accuracy 0.6673 | Validation F1 0.7453\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0272 | Training Loss 0.6746 | Training F1 0.6993 | Validation Accuracy 0.6635 | Validation F1 0.7213\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0272 | Training Loss 0.6405 | Training F1 0.6685 | Validation Accuracy 0.6806 | Validation F1 0.6978\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0271 | Training Loss 0.6424 | Training F1 0.5980 | Validation Accuracy 0.6692 | Validation F1 0.6463\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0271 | Training Loss 0.6597 | Training F1 0.5747 | Validation Accuracy 0.6521 | Validation F1 0.6098\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0270 | Training Loss 0.6627 | Training F1 0.6246 | Validation Accuracy 0.6654 | Validation F1 0.6437\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0270 | Training Loss 0.6375 | Training F1 0.7087 | Validation Accuracy 0.6920 | Validation F1 0.7148\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0270 | Training Loss 0.6045 | Training F1 0.7450 | Validation Accuracy 0.7015 | Validation F1 0.7603\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0269 | Training Loss 0.6037 | Training F1 0.7423 | Validation Accuracy 0.6730 | Validation F1 0.7507\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0269 | Training Loss 0.6180 | Training F1 0.7484 | Validation Accuracy 0.6863 | Validation F1 0.7584\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0269 | Training Loss 0.6081 | Training F1 0.7482 | Validation Accuracy 0.7129 | Validation F1 0.7659\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0268 | Training Loss 0.5912 | Training F1 0.7395 | Validation Accuracy 0.7091 | Validation F1 0.7394\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0268 | Training Loss 0.5858 | Training F1 0.7157 | Validation Accuracy 0.7110 | Validation F1 0.7246\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0268 | Training Loss 0.5886 | Training F1 0.7142 | Validation Accuracy 0.7186 | Validation F1 0.7269\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0268 | Training Loss 0.5858 | Training F1 0.7365 | Validation Accuracy 0.7129 | Validation F1 0.7327\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0267 | Training Loss 0.5751 | Training F1 0.7486 | Validation Accuracy 0.7167 | Validation F1 0.7504\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0267 | Training Loss 0.5629 | Training F1 0.7560 | Validation Accuracy 0.7243 | Validation F1 0.7673\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0267 | Training Loss 0.5566 | Training F1 0.7639 | Validation Accuracy 0.7243 | Validation F1 0.7731\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0267 | Training Loss 0.5544 | Training F1 0.7640 | Validation Accuracy 0.7300 | Validation F1 0.7739\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0267 | Training Loss 0.5475 | Training F1 0.7554 | Validation Accuracy 0.7433 | Validation F1 0.7723\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0268 | Training Loss 0.5393 | Training F1 0.7410 | Validation Accuracy 0.7186 | Validation F1 0.7357\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0268 | Training Loss 0.5401 | Training F1 0.7468 | Validation Accuracy 0.7243 | Validation F1 0.7425\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0267 | Training Loss 0.5353 | Training F1 0.7607 | Validation Accuracy 0.7452 | Validation F1 0.7729\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0267 | Training Loss 0.5278 | Training F1 0.7723 | Validation Accuracy 0.7300 | Validation F1 0.7717\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0267 | Training Loss 0.5270 | Training F1 0.7726 | Validation Accuracy 0.7395 | Validation F1 0.7787\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0267 | Training Loss 0.5217 | Training F1 0.7663 | Validation Accuracy 0.7433 | Validation F1 0.7700\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0267 | Training Loss 0.5151 | Training F1 0.7609 | Validation Accuracy 0.7357 | Validation F1 0.7549\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0267 | Training Loss 0.5135 | Training F1 0.7672 | Validation Accuracy 0.7338 | Validation F1 0.7569\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0267 | Training Loss 0.5085 | Training F1 0.7779 | Validation Accuracy 0.7376 | Validation F1 0.7677\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0266 | Training Loss 0.5036 | Training F1 0.7871 | Validation Accuracy 0.7471 | Validation F1 0.7809\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0266 | Training Loss 0.5013 | Training F1 0.7854 | Validation Accuracy 0.7452 | Validation F1 0.7767\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0266 | Training Loss 0.4970 | Training F1 0.7853 | Validation Accuracy 0.7471 | Validation F1 0.7711\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0266 | Training Loss 0.4934 | Training F1 0.7837 | Validation Accuracy 0.7452 | Validation F1 0.7657\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0266 | Training Loss 0.4913 | Training F1 0.7836 | Validation Accuracy 0.7510 | Validation F1 0.7737\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0266 | Training Loss 0.4876 | Training F1 0.7917 | Validation Accuracy 0.7529 | Validation F1 0.7819\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0266 | Training Loss 0.4846 | Training F1 0.7900 | Validation Accuracy 0.7548 | Validation F1 0.7839\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0266 | Training Loss 0.4826 | Training F1 0.7918 | Validation Accuracy 0.7567 | Validation F1 0.7838\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0266 | Training Loss 0.4789 | Training F1 0.7925 | Validation Accuracy 0.7586 | Validation F1 0.7791\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0265 | Training Loss 0.4766 | Training F1 0.7935 | Validation Accuracy 0.7643 | Validation F1 0.7840\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0265 | Training Loss 0.4740 | Training F1 0.7960 | Validation Accuracy 0.7586 | Validation F1 0.7829\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0265 | Training Loss 0.4705 | Training F1 0.7942 | Validation Accuracy 0.7586 | Validation F1 0.7858\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0265 | Training Loss 0.4683 | Training F1 0.7971 | Validation Accuracy 0.7624 | Validation F1 0.7871\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0265 | Training Loss 0.4649 | Training F1 0.7984 | Validation Accuracy 0.7624 | Validation F1 0.7849\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0265 | Training Loss 0.4620 | Training F1 0.7995 | Validation Accuracy 0.7624 | Validation F1 0.7856\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0265 | Training Loss 0.4591 | Training F1 0.7998 | Validation Accuracy 0.7643 | Validation F1 0.7877\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0265 | Training Loss 0.4562 | Training F1 0.8000 | Validation Accuracy 0.7605 | Validation F1 0.7842\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0265 | Training Loss 0.4535 | Training F1 0.8016 | Validation Accuracy 0.7624 | Validation F1 0.7818\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0265 | Training Loss 0.4508 | Training F1 0.8045 | Validation Accuracy 0.7624 | Validation F1 0.7841\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0265 | Training Loss 0.4480 | Training F1 0.8050 | Validation Accuracy 0.7700 | Validation F1 0.7917\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0265 | Training Loss 0.4457 | Training F1 0.8028 | Validation Accuracy 0.7681 | Validation F1 0.7882\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0265 | Training Loss 0.4434 | Training F1 0.8056 | Validation Accuracy 0.7719 | Validation F1 0.7924\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0265 | Training Loss 0.4409 | Training F1 0.8095 | Validation Accuracy 0.7700 | Validation F1 0.7917\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0265 | Training Loss 0.4387 | Training F1 0.8058 | Validation Accuracy 0.7738 | Validation F1 0.7923\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0265 | Training Loss 0.4367 | Training F1 0.8128 | Validation Accuracy 0.7719 | Validation F1 0.7959\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0265 | Training Loss 0.4348 | Training F1 0.8079 | Validation Accuracy 0.7738 | Validation F1 0.7916\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0264 | Training Loss 0.4325 | Training F1 0.8161 | Validation Accuracy 0.7738 | Validation F1 0.7966\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0266 | Training Loss 0.4303 | Training F1 0.8147 | Validation Accuracy 0.7681 | Validation F1 0.7867\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0266 | Training Loss 0.4278 | Training F1 0.8169 | Validation Accuracy 0.7795 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0266 | Training Loss 0.4254 | Training F1 0.8175 | Validation Accuracy 0.7795 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0266 | Training Loss 0.4233 | Training F1 0.8193 | Validation Accuracy 0.7795 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0266 | Training Loss 0.4209 | Training F1 0.8195 | Validation Accuracy 0.7719 | Validation F1 0.7924\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0266 | Training Loss 0.4188 | Training F1 0.8224 | Validation Accuracy 0.7757 | Validation F1 0.7979\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0266 | Training Loss 0.4167 | Training F1 0.8212 | Validation Accuracy 0.7814 | Validation F1 0.7965\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0266 | Training Loss 0.4156 | Training F1 0.8267 | Validation Accuracy 0.7624 | Validation F1 0.8006\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0266 | Training Loss 0.4303 | Training F1 0.8032 | Validation Accuracy 0.7738 | Validation F1 0.7750\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0266 | Training Loss 0.4323 | Training F1 0.8271 | Validation Accuracy 0.7852 | Validation F1 0.8062\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0266 | Training Loss 0.4095 | Training F1 0.8314 | Validation Accuracy 0.7719 | Validation F1 0.8052\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0266 | Training Loss 0.4227 | Training F1 0.8253 | Validation Accuracy 0.7871 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0266 | Training Loss 0.4076 | Training F1 0.8095 | Validation Accuracy 0.7795 | Validation F1 0.7852\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0266 | Training Loss 0.4177 | Training F1 0.8310 | Validation Accuracy 0.7871 | Validation F1 0.8102\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0266 | Training Loss 0.4057 | Training F1 0.8346 | Validation Accuracy 0.7852 | Validation F1 0.8132\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0266 | Training Loss 0.4123 | Training F1 0.8260 | Validation Accuracy 0.7833 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0266 | Training Loss 0.4026 | Training F1 0.8200 | Validation Accuracy 0.7852 | Validation F1 0.7934\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0266 | Training Loss 0.4087 | Training F1 0.8302 | Validation Accuracy 0.7909 | Validation F1 0.8123\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0266 | Training Loss 0.4000 | Training F1 0.8326 | Validation Accuracy 0.7814 | Validation F1 0.8080\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0266 | Training Loss 0.4046 | Training F1 0.8327 | Validation Accuracy 0.7852 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0266 | Training Loss 0.3973 | Training F1 0.8275 | Validation Accuracy 0.7871 | Validation F1 0.7964\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0266 | Training Loss 0.4012 | Training F1 0.8357 | Validation Accuracy 0.7966 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0266 | Training Loss 0.3947 | Training F1 0.8369 | Validation Accuracy 0.7833 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0266 | Training Loss 0.3975 | Training F1 0.8332 | Validation Accuracy 0.7928 | Validation F1 0.8091\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0265 | Training Loss 0.3922 | Training F1 0.8312 | Validation Accuracy 0.7871 | Validation F1 0.7986\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f684166d0>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0282 | Training Loss 18.6454 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0276 | Training Loss 9.5994 | Training F1 0.7029 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0273 | Training Loss 3.3362 | Training F1 0.0773 | Validation Accuracy 0.4544 | Validation F1 0.0401\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0271 | Training Loss 1.7978 | Training F1 0.0035 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0269 | Training Loss 2.0340 | Training F1 0.0846 | Validation Accuracy 0.4335 | Validation F1 0.0629\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0268 | Training Loss 1.0365 | Training F1 0.6415 | Validation Accuracy 0.5722 | Validation F1 0.6411\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0267 | Training Loss 0.7348 | Training F1 0.6922 | Validation Accuracy 0.5760 | Validation F1 0.6999\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0266 | Training Loss 0.7657 | Training F1 0.7010 | Validation Accuracy 0.5817 | Validation F1 0.7090\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0266 | Training Loss 0.7672 | Training F1 0.7046 | Validation Accuracy 0.5951 | Validation F1 0.7118\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0268 | Training Loss 0.7221 | Training F1 0.7063 | Validation Accuracy 0.6198 | Validation F1 0.7076\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0268 | Training Loss 0.6669 | Training F1 0.6804 | Validation Accuracy 0.6331 | Validation F1 0.6820\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0267 | Training Loss 0.6362 | Training F1 0.6023 | Validation Accuracy 0.6293 | Validation F1 0.6257\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0267 | Training Loss 0.6455 | Training F1 0.5653 | Validation Accuracy 0.6160 | Validation F1 0.5878\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0266 | Training Loss 0.6532 | Training F1 0.6391 | Validation Accuracy 0.6331 | Validation F1 0.6338\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0266 | Training Loss 0.6251 | Training F1 0.7100 | Validation Accuracy 0.6673 | Validation F1 0.7164\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0266 | Training Loss 0.6063 | Training F1 0.7313 | Validation Accuracy 0.6616 | Validation F1 0.7375\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0266 | Training Loss 0.6152 | Training F1 0.7356 | Validation Accuracy 0.6749 | Validation F1 0.7421\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0266 | Training Loss 0.5988 | Training F1 0.7281 | Validation Accuracy 0.7053 | Validation F1 0.7471\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0266 | Training Loss 0.5806 | Training F1 0.7141 | Validation Accuracy 0.6996 | Validation F1 0.7218\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0266 | Training Loss 0.5760 | Training F1 0.7063 | Validation Accuracy 0.7034 | Validation F1 0.7164\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0265 | Training Loss 0.5713 | Training F1 0.7257 | Validation Accuracy 0.7129 | Validation F1 0.7299\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0265 | Training Loss 0.5596 | Training F1 0.7373 | Validation Accuracy 0.7262 | Validation F1 0.7551\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0265 | Training Loss 0.5452 | Training F1 0.7480 | Validation Accuracy 0.7300 | Validation F1 0.7593\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0265 | Training Loss 0.5343 | Training F1 0.7657 | Validation Accuracy 0.7395 | Validation F1 0.7815\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0265 | Training Loss 0.5285 | Training F1 0.7645 | Validation Accuracy 0.7433 | Validation F1 0.7761\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0265 | Training Loss 0.5213 | Training F1 0.7556 | Validation Accuracy 0.7471 | Validation F1 0.7687\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0265 | Training Loss 0.5190 | Training F1 0.7863 | Validation Accuracy 0.7319 | Validation F1 0.7867\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0264 | Training Loss 0.5279 | Training F1 0.7731 | Validation Accuracy 0.7510 | Validation F1 0.7835\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0264 | Training Loss 0.5048 | Training F1 0.7563 | Validation Accuracy 0.7586 | Validation F1 0.7678\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0264 | Training Loss 0.5159 | Training F1 0.7833 | Validation Accuracy 0.7529 | Validation F1 0.7876\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0264 | Training Loss 0.4939 | Training F1 0.7918 | Validation Accuracy 0.7414 | Validation F1 0.7914\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0264 | Training Loss 0.5026 | Training F1 0.7900 | Validation Accuracy 0.7662 | Validation F1 0.7953\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0264 | Training Loss 0.4838 | Training F1 0.7768 | Validation Accuracy 0.7681 | Validation F1 0.7806\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0264 | Training Loss 0.4924 | Training F1 0.7928 | Validation Accuracy 0.7624 | Validation F1 0.7961\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0264 | Training Loss 0.4748 | Training F1 0.7976 | Validation Accuracy 0.7567 | Validation F1 0.8006\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0264 | Training Loss 0.4820 | Training F1 0.7910 | Validation Accuracy 0.7719 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0264 | Training Loss 0.4676 | Training F1 0.7857 | Validation Accuracy 0.7738 | Validation F1 0.7930\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0264 | Training Loss 0.4737 | Training F1 0.8010 | Validation Accuracy 0.7700 | Validation F1 0.8033\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0264 | Training Loss 0.4624 | Training F1 0.8044 | Validation Accuracy 0.7624 | Validation F1 0.8038\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0263 | Training Loss 0.4672 | Training F1 0.8050 | Validation Accuracy 0.7776 | Validation F1 0.8079\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0263 | Training Loss 0.4570 | Training F1 0.7890 | Validation Accuracy 0.7776 | Validation F1 0.7951\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0263 | Training Loss 0.4612 | Training F1 0.8067 | Validation Accuracy 0.7795 | Validation F1 0.8098\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0263 | Training Loss 0.4516 | Training F1 0.8111 | Validation Accuracy 0.7719 | Validation F1 0.8095\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0263 | Training Loss 0.4535 | Training F1 0.8091 | Validation Accuracy 0.7871 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0263 | Training Loss 0.4451 | Training F1 0.7987 | Validation Accuracy 0.7890 | Validation F1 0.8070\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0263 | Training Loss 0.4475 | Training F1 0.8126 | Validation Accuracy 0.7966 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0267 | Training Loss 0.4401 | Training F1 0.8111 | Validation Accuracy 0.7795 | Validation F1 0.8147\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0267 | Training Loss 0.4415 | Training F1 0.8163 | Validation Accuracy 0.7909 | Validation F1 0.8167\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0267 | Training Loss 0.4350 | Training F1 0.8123 | Validation Accuracy 0.7985 | Validation F1 0.8191\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0267 | Training Loss 0.4358 | Training F1 0.8158 | Validation Accuracy 0.7890 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0267 | Training Loss 0.4300 | Training F1 0.8152 | Validation Accuracy 0.7776 | Validation F1 0.8116\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0267 | Training Loss 0.4297 | Training F1 0.8189 | Validation Accuracy 0.7909 | Validation F1 0.8185\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0267 | Training Loss 0.4247 | Training F1 0.8223 | Validation Accuracy 0.8004 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0266 | Training Loss 0.4235 | Training F1 0.8212 | Validation Accuracy 0.7833 | Validation F1 0.8161\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0266 | Training Loss 0.4205 | Training F1 0.8230 | Validation Accuracy 0.7928 | Validation F1 0.8228\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0266 | Training Loss 0.4166 | Training F1 0.8251 | Validation Accuracy 0.8080 | Validation F1 0.8291\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0266 | Training Loss 0.4148 | Training F1 0.8236 | Validation Accuracy 0.8004 | Validation F1 0.8282\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0266 | Training Loss 0.4111 | Training F1 0.8263 | Validation Accuracy 0.7947 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0266 | Training Loss 0.4091 | Training F1 0.8282 | Validation Accuracy 0.8099 | Validation F1 0.8316\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0266 | Training Loss 0.4067 | Training F1 0.8295 | Validation Accuracy 0.8061 | Validation F1 0.8317\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0266 | Training Loss 0.4031 | Training F1 0.8321 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0266 | Training Loss 0.4009 | Training F1 0.8289 | Validation Accuracy 0.8156 | Validation F1 0.8359\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0266 | Training Loss 0.3991 | Training F1 0.8335 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0266 | Training Loss 0.3960 | Training F1 0.8349 | Validation Accuracy 0.8099 | Validation F1 0.8339\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0266 | Training Loss 0.3930 | Training F1 0.8318 | Validation Accuracy 0.8156 | Validation F1 0.8364\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0266 | Training Loss 0.3910 | Training F1 0.8358 | Validation Accuracy 0.8004 | Validation F1 0.8276\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0265 | Training Loss 0.3894 | Training F1 0.8363 | Validation Accuracy 0.8213 | Validation F1 0.8390\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0265 | Training Loss 0.3878 | Training F1 0.8399 | Validation Accuracy 0.7966 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0266 | Training Loss 0.3860 | Training F1 0.8348 | Validation Accuracy 0.8251 | Validation F1 0.8430\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0266 | Training Loss 0.3837 | Training F1 0.8450 | Validation Accuracy 0.7985 | Validation F1 0.8257\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0266 | Training Loss 0.3814 | Training F1 0.8354 | Validation Accuracy 0.8213 | Validation F1 0.8396\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0266 | Training Loss 0.3793 | Training F1 0.8464 | Validation Accuracy 0.8004 | Validation F1 0.8270\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0266 | Training Loss 0.3773 | Training F1 0.8389 | Validation Accuracy 0.8194 | Validation F1 0.8382\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0266 | Training Loss 0.3753 | Training F1 0.8482 | Validation Accuracy 0.8080 | Validation F1 0.8325\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0266 | Training Loss 0.3730 | Training F1 0.8456 | Validation Accuracy 0.8137 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0266 | Training Loss 0.3713 | Training F1 0.8505 | Validation Accuracy 0.7966 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0265 | Training Loss 0.3702 | Training F1 0.8352 | Validation Accuracy 0.8175 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0265 | Training Loss 0.3706 | Training F1 0.8510 | Validation Accuracy 0.7928 | Validation F1 0.8245\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0265 | Training Loss 0.3755 | Training F1 0.8177 | Validation Accuracy 0.8308 | Validation F1 0.8385\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0265 | Training Loss 0.3854 | Training F1 0.8469 | Validation Accuracy 0.7928 | Validation F1 0.8273\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0265 | Training Loss 0.3865 | Training F1 0.8390 | Validation Accuracy 0.8289 | Validation F1 0.8443\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0265 | Training Loss 0.3667 | Training F1 0.8418 | Validation Accuracy 0.8232 | Validation F1 0.8405\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0265 | Training Loss 0.3628 | Training F1 0.8524 | Validation Accuracy 0.7909 | Validation F1 0.8248\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0265 | Training Loss 0.3773 | Training F1 0.8253 | Validation Accuracy 0.8175 | Validation F1 0.8273\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0265 | Training Loss 0.3733 | Training F1 0.8576 | Validation Accuracy 0.8004 | Validation F1 0.8270\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0265 | Training Loss 0.3576 | Training F1 0.8581 | Validation Accuracy 0.7966 | Validation F1 0.8249\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0265 | Training Loss 0.3612 | Training F1 0.8349 | Validation Accuracy 0.8289 | Validation F1 0.8410\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0265 | Training Loss 0.3653 | Training F1 0.8609 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0265 | Training Loss 0.3543 | Training F1 0.8602 | Validation Accuracy 0.8004 | Validation F1 0.8276\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0265 | Training Loss 0.3538 | Training F1 0.8402 | Validation Accuracy 0.8251 | Validation F1 0.8392\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0265 | Training Loss 0.3594 | Training F1 0.8584 | Validation Accuracy 0.8004 | Validation F1 0.8282\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0265 | Training Loss 0.3523 | Training F1 0.8614 | Validation Accuracy 0.7966 | Validation F1 0.8231\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0265 | Training Loss 0.3480 | Training F1 0.8473 | Validation Accuracy 0.8194 | Validation F1 0.8342\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0265 | Training Loss 0.3528 | Training F1 0.8605 | Validation Accuracy 0.8023 | Validation F1 0.8295\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0265 | Training Loss 0.3486 | Training F1 0.8642 | Validation Accuracy 0.8080 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0265 | Training Loss 0.3435 | Training F1 0.8527 | Validation Accuracy 0.8175 | Validation F1 0.8345\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0265 | Training Loss 0.3467 | Training F1 0.8654 | Validation Accuracy 0.7985 | Validation F1 0.8274\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0265 | Training Loss 0.3493 | Training F1 0.8541 | Validation Accuracy 0.8270 | Validation F1 0.8412\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0265 | Training Loss 0.3452 | Training F1 0.8656 | Validation Accuracy 0.8042 | Validation F1 0.8286\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0265 | Training Loss 0.3392 | Training F1 0.8685 | Validation Accuracy 0.8080 | Validation F1 0.8331\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f68416e40>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0285 | Training Loss 16.9175 | Training F1 0.4947 | Validation Accuracy 0.5038 | Validation F1 0.4505\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0277 | Training Loss 1.1216 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0273 | Training Loss 7.9348 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0271 | Training Loss 6.9088 | Training F1 0.7019 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0270 | Training Loss 3.6431 | Training F1 0.7050 | Validation Accuracy 0.6939 | Validation F1 0.7321\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0268 | Training Loss 0.6920 | Training F1 0.0035 | Validation Accuracy 0.4601 | Validation F1 0.0070\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0267 | Training Loss 2.7367 | Training F1 0.0018 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0269 | Training Loss 2.6220 | Training F1 0.1094 | Validation Accuracy 0.4905 | Validation F1 0.1126\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0268 | Training Loss 1.4911 | Training F1 0.6403 | Validation Accuracy 0.6901 | Validation F1 0.6667\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0267 | Training Loss 0.6506 | Training F1 0.7292 | Validation Accuracy 0.6236 | Validation F1 0.7332\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0270 | Training Loss 0.7928 | Training F1 0.7107 | Validation Accuracy 0.5665 | Validation F1 0.7121\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0269 | Training Loss 1.1310 | Training F1 0.7073 | Validation Accuracy 0.5589 | Validation F1 0.7085\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0269 | Training Loss 1.1815 | Training F1 0.7166 | Validation Accuracy 0.5837 | Validation F1 0.7189\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0268 | Training Loss 0.9637 | Training F1 0.7345 | Validation Accuracy 0.6464 | Validation F1 0.7395\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0267 | Training Loss 0.6723 | Training F1 0.6718 | Validation Accuracy 0.7072 | Validation F1 0.7072\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0270 | Training Loss 0.5999 | Training F1 0.4494 | Validation Accuracy 0.6160 | Validation F1 0.4794\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0270 | Training Loss 0.7946 | Training F1 0.3391 | Validation Accuracy 0.5856 | Validation F1 0.3978\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0269 | Training Loss 0.8788 | Training F1 0.4797 | Validation Accuracy 0.6160 | Validation F1 0.4950\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0269 | Training Loss 0.7620 | Training F1 0.6515 | Validation Accuracy 0.6901 | Validation F1 0.6653\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0268 | Training Loss 0.6051 | Training F1 0.7400 | Validation Accuracy 0.7015 | Validation F1 0.7447\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0268 | Training Loss 0.5699 | Training F1 0.7468 | Validation Accuracy 0.6692 | Validation F1 0.7535\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0267 | Training Loss 0.6376 | Training F1 0.7400 | Validation Accuracy 0.6369 | Validation F1 0.7401\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0267 | Training Loss 0.6956 | Training F1 0.7412 | Validation Accuracy 0.6369 | Validation F1 0.7387\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0267 | Training Loss 0.6856 | Training F1 0.7459 | Validation Accuracy 0.6692 | Validation F1 0.7528\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0267 | Training Loss 0.6264 | Training F1 0.7533 | Validation Accuracy 0.6844 | Validation F1 0.7422\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0267 | Training Loss 0.5698 | Training F1 0.7332 | Validation Accuracy 0.7205 | Validation F1 0.7370\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0266 | Training Loss 0.5547 | Training F1 0.6783 | Validation Accuracy 0.7110 | Validation F1 0.6996\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0266 | Training Loss 0.5792 | Training F1 0.6325 | Validation Accuracy 0.6901 | Validation F1 0.6554\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0266 | Training Loss 0.6067 | Training F1 0.6228 | Validation Accuracy 0.6844 | Validation F1 0.6453\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0266 | Training Loss 0.6112 | Training F1 0.6541 | Validation Accuracy 0.7034 | Validation F1 0.6777\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0266 | Training Loss 0.5918 | Training F1 0.6984 | Validation Accuracy 0.7281 | Validation F1 0.7245\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0266 | Training Loss 0.5644 | Training F1 0.7384 | Validation Accuracy 0.7224 | Validation F1 0.7384\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0265 | Training Loss 0.5469 | Training F1 0.7562 | Validation Accuracy 0.7167 | Validation F1 0.7537\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0265 | Training Loss 0.5454 | Training F1 0.7581 | Validation Accuracy 0.6939 | Validation F1 0.7488\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0265 | Training Loss 0.5542 | Training F1 0.7586 | Validation Accuracy 0.6920 | Validation F1 0.7560\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0265 | Training Loss 0.5627 | Training F1 0.7604 | Validation Accuracy 0.6958 | Validation F1 0.7598\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0265 | Training Loss 0.5624 | Training F1 0.7603 | Validation Accuracy 0.6977 | Validation F1 0.7573\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0265 | Training Loss 0.5532 | Training F1 0.7632 | Validation Accuracy 0.7034 | Validation F1 0.7516\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0265 | Training Loss 0.5404 | Training F1 0.7651 | Validation Accuracy 0.7319 | Validation F1 0.7638\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0265 | Training Loss 0.5310 | Training F1 0.7540 | Validation Accuracy 0.7338 | Validation F1 0.7544\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0264 | Training Loss 0.5284 | Training F1 0.7395 | Validation Accuracy 0.7414 | Validation F1 0.7472\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0264 | Training Loss 0.5303 | Training F1 0.7397 | Validation Accuracy 0.7452 | Validation F1 0.7452\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0264 | Training Loss 0.5316 | Training F1 0.7397 | Validation Accuracy 0.7433 | Validation F1 0.7429\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0264 | Training Loss 0.5282 | Training F1 0.7502 | Validation Accuracy 0.7452 | Validation F1 0.7537\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0264 | Training Loss 0.5189 | Training F1 0.7719 | Validation Accuracy 0.7452 | Validation F1 0.7690\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0264 | Training Loss 0.5112 | Training F1 0.7733 | Validation Accuracy 0.7414 | Validation F1 0.7756\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0264 | Training Loss 0.5133 | Training F1 0.7809 | Validation Accuracy 0.7529 | Validation F1 0.7782\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0264 | Training Loss 0.5053 | Training F1 0.7676 | Validation Accuracy 0.7567 | Validation F1 0.7698\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0264 | Training Loss 0.5037 | Training F1 0.7720 | Validation Accuracy 0.7624 | Validation F1 0.7795\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0264 | Training Loss 0.4986 | Training F1 0.7812 | Validation Accuracy 0.7510 | Validation F1 0.7813\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0264 | Training Loss 0.4994 | Training F1 0.7793 | Validation Accuracy 0.7643 | Validation F1 0.7840\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0264 | Training Loss 0.4934 | Training F1 0.7672 | Validation Accuracy 0.7643 | Validation F1 0.7745\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0264 | Training Loss 0.4930 | Training F1 0.7810 | Validation Accuracy 0.7662 | Validation F1 0.7831\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0264 | Training Loss 0.4883 | Training F1 0.7902 | Validation Accuracy 0.7586 | Validation F1 0.7844\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0264 | Training Loss 0.4868 | Training F1 0.7922 | Validation Accuracy 0.7662 | Validation F1 0.7897\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0264 | Training Loss 0.4839 | Training F1 0.7881 | Validation Accuracy 0.7738 | Validation F1 0.7901\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0264 | Training Loss 0.4806 | Training F1 0.7853 | Validation Accuracy 0.7776 | Validation F1 0.7914\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0264 | Training Loss 0.4793 | Training F1 0.7895 | Validation Accuracy 0.7833 | Validation F1 0.7986\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0264 | Training Loss 0.4766 | Training F1 0.7925 | Validation Accuracy 0.7833 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0264 | Training Loss 0.4747 | Training F1 0.7974 | Validation Accuracy 0.7814 | Validation F1 0.8014\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0264 | Training Loss 0.4731 | Training F1 0.7942 | Validation Accuracy 0.7795 | Validation F1 0.7972\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0264 | Training Loss 0.4704 | Training F1 0.7928 | Validation Accuracy 0.7890 | Validation F1 0.8035\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0264 | Training Loss 0.4688 | Training F1 0.7936 | Validation Accuracy 0.7871 | Validation F1 0.8028\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0264 | Training Loss 0.4666 | Training F1 0.7974 | Validation Accuracy 0.7814 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0264 | Training Loss 0.4645 | Training F1 0.8012 | Validation Accuracy 0.7776 | Validation F1 0.7972\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0264 | Training Loss 0.4629 | Training F1 0.7975 | Validation Accuracy 0.7814 | Validation F1 0.7993\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0263 | Training Loss 0.4605 | Training F1 0.7963 | Validation Accuracy 0.7833 | Validation F1 0.8007\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0263 | Training Loss 0.4589 | Training F1 0.8035 | Validation Accuracy 0.7852 | Validation F1 0.8042\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0263 | Training Loss 0.4568 | Training F1 0.8042 | Validation Accuracy 0.7833 | Validation F1 0.8034\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0263 | Training Loss 0.4553 | Training F1 0.8025 | Validation Accuracy 0.7890 | Validation F1 0.8070\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0263 | Training Loss 0.4535 | Training F1 0.8039 | Validation Accuracy 0.7890 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0263 | Training Loss 0.4519 | Training F1 0.8098 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0263 | Training Loss 0.4504 | Training F1 0.8088 | Validation Accuracy 0.7909 | Validation F1 0.8097\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0263 | Training Loss 0.4485 | Training F1 0.8064 | Validation Accuracy 0.7947 | Validation F1 0.8118\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0263 | Training Loss 0.4470 | Training F1 0.8147 | Validation Accuracy 0.7871 | Validation F1 0.8076\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0263 | Training Loss 0.4454 | Training F1 0.8104 | Validation Accuracy 0.7928 | Validation F1 0.8098\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0263 | Training Loss 0.4435 | Training F1 0.8106 | Validation Accuracy 0.7947 | Validation F1 0.8118\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0263 | Training Loss 0.4417 | Training F1 0.8154 | Validation Accuracy 0.7909 | Validation F1 0.8110\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0263 | Training Loss 0.4399 | Training F1 0.8141 | Validation Accuracy 0.7985 | Validation F1 0.8153\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0263 | Training Loss 0.4382 | Training F1 0.8149 | Validation Accuracy 0.7985 | Validation F1 0.8153\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0263 | Training Loss 0.4363 | Training F1 0.8155 | Validation Accuracy 0.7966 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0263 | Training Loss 0.4346 | Training F1 0.8136 | Validation Accuracy 0.7947 | Validation F1 0.8112\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0263 | Training Loss 0.4329 | Training F1 0.8166 | Validation Accuracy 0.7928 | Validation F1 0.8117\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0263 | Training Loss 0.4312 | Training F1 0.8128 | Validation Accuracy 0.7966 | Validation F1 0.8126\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0263 | Training Loss 0.4295 | Training F1 0.8156 | Validation Accuracy 0.7985 | Validation F1 0.8153\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0263 | Training Loss 0.4278 | Training F1 0.8202 | Validation Accuracy 0.7966 | Validation F1 0.8146\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0263 | Training Loss 0.4264 | Training F1 0.8185 | Validation Accuracy 0.8023 | Validation F1 0.8188\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0263 | Training Loss 0.4250 | Training F1 0.8241 | Validation Accuracy 0.7966 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0263 | Training Loss 0.4237 | Training F1 0.8178 | Validation Accuracy 0.8061 | Validation F1 0.8198\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0263 | Training Loss 0.4229 | Training F1 0.8299 | Validation Accuracy 0.8061 | Validation F1 0.8277\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0263 | Training Loss 0.4232 | Training F1 0.8120 | Validation Accuracy 0.8080 | Validation F1 0.8193\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0263 | Training Loss 0.4226 | Training F1 0.8287 | Validation Accuracy 0.8004 | Validation F1 0.8205\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0263 | Training Loss 0.4187 | Training F1 0.8279 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0263 | Training Loss 0.4170 | Training F1 0.8176 | Validation Accuracy 0.8042 | Validation F1 0.8171\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0263 | Training Loss 0.4175 | Training F1 0.8291 | Validation Accuracy 0.8042 | Validation F1 0.8233\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0263 | Training Loss 0.4146 | Training F1 0.8290 | Validation Accuracy 0.8042 | Validation F1 0.8233\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0263 | Training Loss 0.4128 | Training F1 0.8221 | Validation Accuracy 0.8080 | Validation F1 0.8206\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0263 | Training Loss 0.4124 | Training F1 0.8320 | Validation Accuracy 0.8042 | Validation F1 0.8239\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0263 | Training Loss 0.4104 | Training F1 0.8315 | Validation Accuracy 0.8004 | Validation F1 0.8199\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0263 | Training Loss 0.4092 | Training F1 0.8255 | Validation Accuracy 0.8080 | Validation F1 0.8212\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7fa002a356d0>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0282 | Training Loss 22.7296 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0274 | Training Loss 10.4536 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0271 | Training Loss 3.0150 | Training F1 0.7021 | Validation Accuracy 0.5456 | Validation F1 0.7046\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0269 | Training Loss 2.4223 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0269 | Training Loss 2.9348 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0267 | Training Loss 2.2401 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.6999\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0276 | Training Loss 1.3370 | Training F1 0.6784 | Validation Accuracy 0.5779 | Validation F1 0.6696\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0274 | Training Loss 0.7224 | Training F1 0.1170 | Validation Accuracy 0.4715 | Validation F1 0.1090\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0272 | Training Loss 0.9685 | Training F1 0.0070 | Validation Accuracy 0.4544 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0271 | Training Loss 1.1533 | Training F1 0.0105 | Validation Accuracy 0.4525 | Validation F1 0.0069\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0270 | Training Loss 1.0610 | Training F1 0.1276 | Validation Accuracy 0.4753 | Validation F1 0.1266\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0269 | Training Loss 0.8430 | Training F1 0.5869 | Validation Accuracy 0.6008 | Validation F1 0.5800\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0269 | Training Loss 0.6779 | Training F1 0.7073 | Validation Accuracy 0.6008 | Validation F1 0.7009\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0268 | Training Loss 0.6720 | Training F1 0.7089 | Validation Accuracy 0.5665 | Validation F1 0.7039\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0268 | Training Loss 0.7492 | Training F1 0.7052 | Validation Accuracy 0.5608 | Validation F1 0.7065\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0267 | Training Loss 0.7893 | Training F1 0.7056 | Validation Accuracy 0.5608 | Validation F1 0.7080\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0267 | Training Loss 0.7747 | Training F1 0.7065 | Validation Accuracy 0.5703 | Validation F1 0.7080\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0266 | Training Loss 0.7275 | Training F1 0.7052 | Validation Accuracy 0.5970 | Validation F1 0.7047\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0266 | Training Loss 0.6778 | Training F1 0.6820 | Validation Accuracy 0.6426 | Validation F1 0.7081\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0266 | Training Loss 0.6533 | Training F1 0.6335 | Validation Accuracy 0.6312 | Validation F1 0.6572\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0265 | Training Loss 0.6579 | Training F1 0.5819 | Validation Accuracy 0.6160 | Validation F1 0.5992\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0265 | Training Loss 0.6696 | Training F1 0.5278 | Validation Accuracy 0.5970 | Validation F1 0.5391\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0266 | Training Loss 0.6784 | Training F1 0.5061 | Validation Accuracy 0.5837 | Validation F1 0.5034\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0266 | Training Loss 0.6818 | Training F1 0.5100 | Validation Accuracy 0.5798 | Validation F1 0.4989\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0266 | Training Loss 0.6799 | Training F1 0.5328 | Validation Accuracy 0.5875 | Validation F1 0.5188\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0266 | Training Loss 0.6734 | Training F1 0.5760 | Validation Accuracy 0.6160 | Validation F1 0.5792\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0266 | Training Loss 0.6639 | Training F1 0.6139 | Validation Accuracy 0.6426 | Validation F1 0.6371\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0265 | Training Loss 0.6538 | Training F1 0.6427 | Validation Accuracy 0.6350 | Validation F1 0.6608\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0265 | Training Loss 0.6448 | Training F1 0.6847 | Validation Accuracy 0.6616 | Validation F1 0.7013\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0265 | Training Loss 0.6382 | Training F1 0.7028 | Validation Accuracy 0.6540 | Validation F1 0.7065\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0265 | Training Loss 0.6346 | Training F1 0.7016 | Validation Accuracy 0.6407 | Validation F1 0.7088\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0265 | Training Loss 0.6338 | Training F1 0.7125 | Validation Accuracy 0.6407 | Validation F1 0.7166\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0265 | Training Loss 0.6344 | Training F1 0.7155 | Validation Accuracy 0.6331 | Validation F1 0.7158\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0265 | Training Loss 0.6347 | Training F1 0.7171 | Validation Accuracy 0.6312 | Validation F1 0.7130\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0264 | Training Loss 0.6330 | Training F1 0.7151 | Validation Accuracy 0.6597 | Validation F1 0.7259\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0264 | Training Loss 0.6282 | Training F1 0.7129 | Validation Accuracy 0.6616 | Validation F1 0.7157\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0264 | Training Loss 0.6234 | Training F1 0.7086 | Validation Accuracy 0.6749 | Validation F1 0.7164\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0264 | Training Loss 0.6167 | Training F1 0.7148 | Validation Accuracy 0.6749 | Validation F1 0.7192\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0264 | Training Loss 0.6112 | Training F1 0.7228 | Validation Accuracy 0.6844 | Validation F1 0.7348\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0264 | Training Loss 0.6091 | Training F1 0.7257 | Validation Accuracy 0.6806 | Validation F1 0.7316\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0264 | Training Loss 0.6064 | Training F1 0.7249 | Validation Accuracy 0.6730 | Validation F1 0.7226\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0264 | Training Loss 0.6026 | Training F1 0.7237 | Validation Accuracy 0.6882 | Validation F1 0.7303\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0263 | Training Loss 0.5977 | Training F1 0.7339 | Validation Accuracy 0.6996 | Validation F1 0.7410\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0263 | Training Loss 0.5900 | Training F1 0.7409 | Validation Accuracy 0.7072 | Validation F1 0.7416\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0263 | Training Loss 0.5802 | Training F1 0.7170 | Validation Accuracy 0.7034 | Validation F1 0.7204\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0263 | Training Loss 0.5787 | Training F1 0.7457 | Validation Accuracy 0.7015 | Validation F1 0.7588\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0263 | Training Loss 0.5804 | Training F1 0.7487 | Validation Accuracy 0.7072 | Validation F1 0.7601\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0263 | Training Loss 0.5749 | Training F1 0.7300 | Validation Accuracy 0.7072 | Validation F1 0.7381\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0263 | Training Loss 0.5687 | Training F1 0.7298 | Validation Accuracy 0.7034 | Validation F1 0.7320\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0263 | Training Loss 0.5640 | Training F1 0.7545 | Validation Accuracy 0.7129 | Validation F1 0.7592\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0263 | Training Loss 0.5582 | Training F1 0.7579 | Validation Accuracy 0.7167 | Validation F1 0.7646\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0263 | Training Loss 0.5530 | Training F1 0.7438 | Validation Accuracy 0.7129 | Validation F1 0.7419\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0263 | Training Loss 0.5424 | Training F1 0.7183 | Validation Accuracy 0.7319 | Validation F1 0.7394\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0263 | Training Loss 0.5438 | Training F1 0.7692 | Validation Accuracy 0.7167 | Validation F1 0.7676\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0262 | Training Loss 0.5349 | Training F1 0.7720 | Validation Accuracy 0.7243 | Validation F1 0.7759\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0262 | Training Loss 0.5298 | Training F1 0.7406 | Validation Accuracy 0.7471 | Validation F1 0.7654\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0262 | Training Loss 0.5250 | Training F1 0.7620 | Validation Accuracy 0.7529 | Validation F1 0.7819\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0262 | Training Loss 0.5147 | Training F1 0.7739 | Validation Accuracy 0.7433 | Validation F1 0.7900\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0262 | Training Loss 0.5158 | Training F1 0.7719 | Validation Accuracy 0.7586 | Validation F1 0.7894\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0262 | Training Loss 0.5073 | Training F1 0.7599 | Validation Accuracy 0.7548 | Validation F1 0.7717\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0263 | Training Loss 0.5084 | Training F1 0.7768 | Validation Accuracy 0.7433 | Validation F1 0.7867\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0263 | Training Loss 0.5025 | Training F1 0.7774 | Validation Accuracy 0.7567 | Validation F1 0.7949\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0263 | Training Loss 0.4966 | Training F1 0.7595 | Validation Accuracy 0.7662 | Validation F1 0.7792\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0263 | Training Loss 0.4994 | Training F1 0.7876 | Validation Accuracy 0.7662 | Validation F1 0.8045\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0263 | Training Loss 0.4917 | Training F1 0.7863 | Validation Accuracy 0.7681 | Validation F1 0.8026\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0263 | Training Loss 0.4857 | Training F1 0.7654 | Validation Accuracy 0.7624 | Validation F1 0.7740\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0263 | Training Loss 0.4899 | Training F1 0.7926 | Validation Accuracy 0.7719 | Validation F1 0.8065\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0263 | Training Loss 0.4813 | Training F1 0.7920 | Validation Accuracy 0.7795 | Validation F1 0.8111\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0263 | Training Loss 0.4768 | Training F1 0.7706 | Validation Accuracy 0.7776 | Validation F1 0.7899\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0263 | Training Loss 0.4790 | Training F1 0.7893 | Validation Accuracy 0.7814 | Validation F1 0.8105\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0263 | Training Loss 0.4690 | Training F1 0.7920 | Validation Accuracy 0.7795 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0263 | Training Loss 0.4691 | Training F1 0.7874 | Validation Accuracy 0.7947 | Validation F1 0.8138\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0263 | Training Loss 0.4659 | Training F1 0.7929 | Validation Accuracy 0.7909 | Validation F1 0.8154\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0262 | Training Loss 0.4599 | Training F1 0.7980 | Validation Accuracy 0.7852 | Validation F1 0.8157\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0262 | Training Loss 0.4607 | Training F1 0.7917 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0262 | Training Loss 0.4561 | Training F1 0.7947 | Validation Accuracy 0.7909 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0262 | Training Loss 0.4533 | Training F1 0.8037 | Validation Accuracy 0.7947 | Validation F1 0.8230\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0262 | Training Loss 0.4529 | Training F1 0.8002 | Validation Accuracy 0.7966 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0262 | Training Loss 0.4482 | Training F1 0.7981 | Validation Accuracy 0.7947 | Validation F1 0.8151\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0262 | Training Loss 0.4464 | Training F1 0.8068 | Validation Accuracy 0.7947 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0262 | Training Loss 0.4458 | Training F1 0.8037 | Validation Accuracy 0.7966 | Validation F1 0.8158\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0262 | Training Loss 0.4425 | Training F1 0.8112 | Validation Accuracy 0.8023 | Validation F1 0.8261\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0262 | Training Loss 0.4389 | Training F1 0.8132 | Validation Accuracy 0.8023 | Validation F1 0.8261\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0262 | Training Loss 0.4371 | Training F1 0.8042 | Validation Accuracy 0.7985 | Validation F1 0.8172\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0262 | Training Loss 0.4363 | Training F1 0.8157 | Validation Accuracy 0.7966 | Validation F1 0.8243\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0262 | Training Loss 0.4354 | Training F1 0.8083 | Validation Accuracy 0.8061 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0262 | Training Loss 0.4319 | Training F1 0.8153 | Validation Accuracy 0.7985 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0262 | Training Loss 0.4288 | Training F1 0.8157 | Validation Accuracy 0.7966 | Validation F1 0.8202\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0262 | Training Loss 0.4269 | Training F1 0.8072 | Validation Accuracy 0.8042 | Validation F1 0.8221\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0262 | Training Loss 0.4264 | Training F1 0.8187 | Validation Accuracy 0.7909 | Validation F1 0.8220\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0262 | Training Loss 0.4285 | Training F1 0.8033 | Validation Accuracy 0.8023 | Validation F1 0.8156\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0262 | Training Loss 0.4298 | Training F1 0.8204 | Validation Accuracy 0.7890 | Validation F1 0.8224\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0262 | Training Loss 0.4352 | Training F1 0.8045 | Validation Accuracy 0.8061 | Validation F1 0.8191\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0262 | Training Loss 0.4254 | Training F1 0.8179 | Validation Accuracy 0.8080 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0262 | Training Loss 0.4179 | Training F1 0.8233 | Validation Accuracy 0.7947 | Validation F1 0.8241\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0262 | Training Loss 0.4184 | Training F1 0.8094 | Validation Accuracy 0.8042 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0262 | Training Loss 0.4212 | Training F1 0.8237 | Validation Accuracy 0.7966 | Validation F1 0.8271\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0262 | Training Loss 0.4204 | Training F1 0.8167 | Validation Accuracy 0.8099 | Validation F1 0.8294\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0262 | Training Loss 0.4129 | Training F1 0.8161 | Validation Accuracy 0.8099 | Validation F1 0.8288\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0262 | Training Loss 0.4121 | Training F1 0.8249 | Validation Accuracy 0.7966 | Validation F1 0.8271\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7f9f684166d0>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0285 | Training Loss 1.6217 | Training F1 0.7021 | Validation Accuracy 0.5418 | Validation F1 0.7028\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0283 | Training Loss 11.1545 | Training F1 0.7021 | Validation Accuracy 0.5437 | Validation F1 0.7037\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0277 | Training Loss 8.1530 | Training F1 0.7015 | Validation Accuracy 0.5456 | Validation F1 0.7038\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0274 | Training Loss 3.5539 | Training F1 0.0875 | Validation Accuracy 0.4696 | Validation F1 0.0731\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0271 | Training Loss 1.8561 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0270 | Training Loss 3.0970 | Training F1 0.0786 | Validation Accuracy 0.4715 | Validation F1 0.0608\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0269 | Training Loss 2.0116 | Training F1 0.6374 | Validation Accuracy 0.6426 | Validation F1 0.6453\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0268 | Training Loss 0.8233 | Training F1 0.7126 | Validation Accuracy 0.6008 | Validation F1 0.7193\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0267 | Training Loss 1.1887 | Training F1 0.7049 | Validation Accuracy 0.5665 | Validation F1 0.7136\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0267 | Training Loss 1.5632 | Training F1 0.7054 | Validation Accuracy 0.5760 | Validation F1 0.7174\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0266 | Training Loss 1.4130 | Training F1 0.7160 | Validation Accuracy 0.6179 | Validation F1 0.7228\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0266 | Training Loss 0.9851 | Training F1 0.6971 | Validation Accuracy 0.6654 | Validation F1 0.6976\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0265 | Training Loss 0.7160 | Training F1 0.5190 | Validation Accuracy 0.6141 | Validation F1 0.5418\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0265 | Training Loss 0.8515 | Training F1 0.3674 | Validation Accuracy 0.5779 | Validation F1 0.4032\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0265 | Training Loss 1.0530 | Training F1 0.3707 | Validation Accuracy 0.5779 | Validation F1 0.4032\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0265 | Training Loss 1.0271 | Training F1 0.4949 | Validation Accuracy 0.6103 | Validation F1 0.5036\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0264 | Training Loss 0.8351 | Training F1 0.6482 | Validation Accuracy 0.6730 | Validation F1 0.6627\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0264 | Training Loss 0.6697 | Training F1 0.7358 | Validation Accuracy 0.7053 | Validation F1 0.7471\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0264 | Training Loss 0.6483 | Training F1 0.7372 | Validation Accuracy 0.6559 | Validation F1 0.7411\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0264 | Training Loss 0.7228 | Training F1 0.7301 | Validation Accuracy 0.6274 | Validation F1 0.7351\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0264 | Training Loss 0.7814 | Training F1 0.7334 | Validation Accuracy 0.6293 | Validation F1 0.7376\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0264 | Training Loss 0.7716 | Training F1 0.7402 | Validation Accuracy 0.6559 | Validation F1 0.7469\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0263 | Training Loss 0.7074 | Training F1 0.7507 | Validation Accuracy 0.6996 | Validation F1 0.7591\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0263 | Training Loss 0.6304 | Training F1 0.7416 | Validation Accuracy 0.7357 | Validation F1 0.7616\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0263 | Training Loss 0.5867 | Training F1 0.6843 | Validation Accuracy 0.7072 | Validation F1 0.6980\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0263 | Training Loss 0.5943 | Training F1 0.6114 | Validation Accuracy 0.6787 | Validation F1 0.6302\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0263 | Training Loss 0.6292 | Training F1 0.5734 | Validation Accuracy 0.6559 | Validation F1 0.5877\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0263 | Training Loss 0.6479 | Training F1 0.5996 | Validation Accuracy 0.6654 | Validation F1 0.6054\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0263 | Training Loss 0.6302 | Training F1 0.6754 | Validation Accuracy 0.7053 | Validation F1 0.6791\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0263 | Training Loss 0.5890 | Training F1 0.7289 | Validation Accuracy 0.7529 | Validation F1 0.7645\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0264 | Training Loss 0.5568 | Training F1 0.7635 | Validation Accuracy 0.7567 | Validation F1 0.7922\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0264 | Training Loss 0.5578 | Training F1 0.7691 | Validation Accuracy 0.7281 | Validation F1 0.7803\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0264 | Training Loss 0.5720 | Training F1 0.7718 | Validation Accuracy 0.7300 | Validation F1 0.7835\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0264 | Training Loss 0.5691 | Training F1 0.7727 | Validation Accuracy 0.7490 | Validation F1 0.7918\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0264 | Training Loss 0.5497 | Training F1 0.7624 | Validation Accuracy 0.7700 | Validation F1 0.7932\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0264 | Training Loss 0.5324 | Training F1 0.7370 | Validation Accuracy 0.7567 | Validation F1 0.7630\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0264 | Training Loss 0.5371 | Training F1 0.7393 | Validation Accuracy 0.7586 | Validation F1 0.7617\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0264 | Training Loss 0.5347 | Training F1 0.7563 | Validation Accuracy 0.7776 | Validation F1 0.7899\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0264 | Training Loss 0.5216 | Training F1 0.7683 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0264 | Training Loss 0.5155 | Training F1 0.7780 | Validation Accuracy 0.7776 | Validation F1 0.8072\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0264 | Training Loss 0.5166 | Training F1 0.7825 | Validation Accuracy 0.7643 | Validation F1 0.7987\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0264 | Training Loss 0.5162 | Training F1 0.7833 | Validation Accuracy 0.7757 | Validation F1 0.8059\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0264 | Training Loss 0.5103 | Training F1 0.7792 | Validation Accuracy 0.7852 | Validation F1 0.8094\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0264 | Training Loss 0.5022 | Training F1 0.7782 | Validation Accuracy 0.7814 | Validation F1 0.7950\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0264 | Training Loss 0.4980 | Training F1 0.7745 | Validation Accuracy 0.7890 | Validation F1 0.7978\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0264 | Training Loss 0.4986 | Training F1 0.7781 | Validation Accuracy 0.7909 | Validation F1 0.8000\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0264 | Training Loss 0.4943 | Training F1 0.7866 | Validation Accuracy 0.7909 | Validation F1 0.8070\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0264 | Training Loss 0.4857 | Training F1 0.7939 | Validation Accuracy 0.7966 | Validation F1 0.8183\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0264 | Training Loss 0.4833 | Training F1 0.7975 | Validation Accuracy 0.7966 | Validation F1 0.8190\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0264 | Training Loss 0.4805 | Training F1 0.7924 | Validation Accuracy 0.7985 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0264 | Training Loss 0.4754 | Training F1 0.7886 | Validation Accuracy 0.8080 | Validation F1 0.8219\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0264 | Training Loss 0.4742 | Training F1 0.7948 | Validation Accuracy 0.8061 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0263 | Training Loss 0.4691 | Training F1 0.8024 | Validation Accuracy 0.8061 | Validation F1 0.8259\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0263 | Training Loss 0.4674 | Training F1 0.8036 | Validation Accuracy 0.8156 | Validation F1 0.8336\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0263 | Training Loss 0.4636 | Training F1 0.8039 | Validation Accuracy 0.8175 | Validation F1 0.8298\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0263 | Training Loss 0.4604 | Training F1 0.8049 | Validation Accuracy 0.8194 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0263 | Training Loss 0.4569 | Training F1 0.8094 | Validation Accuracy 0.8213 | Validation F1 0.8390\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0263 | Training Loss 0.4543 | Training F1 0.8080 | Validation Accuracy 0.8194 | Validation F1 0.8370\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0263 | Training Loss 0.4511 | Training F1 0.8090 | Validation Accuracy 0.8232 | Validation F1 0.8360\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0263 | Training Loss 0.4492 | Training F1 0.8095 | Validation Accuracy 0.8232 | Validation F1 0.8383\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0263 | Training Loss 0.4466 | Training F1 0.8106 | Validation Accuracy 0.8175 | Validation F1 0.8351\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0263 | Training Loss 0.4442 | Training F1 0.8108 | Validation Accuracy 0.8213 | Validation F1 0.8362\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0263 | Training Loss 0.4403 | Training F1 0.8102 | Validation Accuracy 0.8156 | Validation F1 0.8289\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0263 | Training Loss 0.4378 | Training F1 0.8183 | Validation Accuracy 0.8099 | Validation F1 0.8282\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0263 | Training Loss 0.4353 | Training F1 0.8163 | Validation Accuracy 0.8118 | Validation F1 0.8260\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0263 | Training Loss 0.4320 | Training F1 0.8185 | Validation Accuracy 0.8137 | Validation F1 0.8281\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0263 | Training Loss 0.4295 | Training F1 0.8213 | Validation Accuracy 0.8137 | Validation F1 0.8304\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0264 | Training Loss 0.4272 | Training F1 0.8079 | Validation Accuracy 0.8023 | Validation F1 0.8149\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0264 | Training Loss 0.4269 | Training F1 0.8223 | Validation Accuracy 0.8023 | Validation F1 0.8267\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0264 | Training Loss 0.4321 | Training F1 0.8066 | Validation Accuracy 0.8023 | Validation F1 0.8129\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0264 | Training Loss 0.4258 | Training F1 0.8152 | Validation Accuracy 0.8080 | Validation F1 0.8212\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0264 | Training Loss 0.4201 | Training F1 0.8237 | Validation Accuracy 0.8118 | Validation F1 0.8319\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0264 | Training Loss 0.4224 | Training F1 0.8221 | Validation Accuracy 0.8118 | Validation F1 0.8278\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0264 | Training Loss 0.4152 | Training F1 0.8093 | Validation Accuracy 0.8004 | Validation F1 0.8073\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0264 | Training Loss 0.4227 | Training F1 0.8241 | Validation Accuracy 0.8137 | Validation F1 0.8328\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0264 | Training Loss 0.4153 | Training F1 0.8276 | Validation Accuracy 0.8099 | Validation F1 0.8288\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0264 | Training Loss 0.4140 | Training F1 0.8118 | Validation Accuracy 0.8042 | Validation F1 0.8144\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0264 | Training Loss 0.4125 | Training F1 0.8163 | Validation Accuracy 0.8042 | Validation F1 0.8157\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0264 | Training Loss 0.4094 | Training F1 0.8311 | Validation Accuracy 0.8118 | Validation F1 0.8308\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0264 | Training Loss 0.4092 | Training F1 0.8291 | Validation Accuracy 0.8080 | Validation F1 0.8262\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0264 | Training Loss 0.4053 | Training F1 0.8144 | Validation Accuracy 0.8061 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0264 | Training Loss 0.4060 | Training F1 0.8266 | Validation Accuracy 0.8061 | Validation F1 0.8179\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0264 | Training Loss 0.4011 | Training F1 0.8296 | Validation Accuracy 0.8137 | Validation F1 0.8322\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0263 | Training Loss 0.4033 | Training F1 0.8265 | Validation Accuracy 0.8137 | Validation F1 0.8269\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0263 | Training Loss 0.3977 | Training F1 0.8215 | Validation Accuracy 0.8061 | Validation F1 0.8152\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0263 | Training Loss 0.3997 | Training F1 0.8316 | Validation Accuracy 0.8175 | Validation F1 0.8322\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0263 | Training Loss 0.3953 | Training F1 0.8350 | Validation Accuracy 0.8156 | Validation F1 0.8325\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0264 | Training Loss 0.3955 | Training F1 0.8276 | Validation Accuracy 0.8118 | Validation F1 0.8223\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0263 | Training Loss 0.3930 | Training F1 0.8292 | Validation Accuracy 0.8080 | Validation F1 0.8193\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0263 | Training Loss 0.3915 | Training F1 0.8338 | Validation Accuracy 0.8194 | Validation F1 0.8359\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0263 | Training Loss 0.3907 | Training F1 0.8327 | Validation Accuracy 0.8213 | Validation F1 0.8357\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0263 | Training Loss 0.3879 | Training F1 0.8335 | Validation Accuracy 0.8080 | Validation F1 0.8180\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0263 | Training Loss 0.3881 | Training F1 0.8341 | Validation Accuracy 0.8194 | Validation F1 0.8342\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0263 | Training Loss 0.3850 | Training F1 0.8320 | Validation Accuracy 0.8232 | Validation F1 0.8383\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0263 | Training Loss 0.3848 | Training F1 0.8347 | Validation Accuracy 0.8118 | Validation F1 0.8235\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0263 | Training Loss 0.3834 | Training F1 0.8397 | Validation Accuracy 0.8194 | Validation F1 0.8325\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0263 | Training Loss 0.3812 | Training F1 0.8346 | Validation Accuracy 0.8251 | Validation F1 0.8403\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0263 | Training Loss 0.3811 | Training F1 0.8379 | Validation Accuracy 0.8118 | Validation F1 0.8242\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0263 | Training Loss 0.3790 | Training F1 0.8381 | Validation Accuracy 0.8137 | Validation F1 0.8269\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0263 | Training Loss 0.3772 | Training F1 0.8369 | Validation Accuracy 0.8232 | Validation F1 0.8394\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n",
      "INFO:root:Read train and test dataset.\n",
      "INFO:root:Further slice the train dataset into train and validation datasets.\n",
      "INFO:root:The training data has shape: (2102, 16).\n",
      "INFO:root:The validation data has shape: (526, 16).\n",
      "INFO:root:The test data has shape: (657, 16).\n",
      "INFO:root:Generate train, validation, and test masks.\n",
      "INFO:root:Number of nodes = 3285\n",
      "INFO:root:Number of features for each node = 14\n",
      "INFO:root:Number of classes = 2.\n",
      "INFO:root:Initializing Model\n",
      "INFO:root:Initialized Model\n",
      "INFO:root:NodeClassification(\n",
      "  (gconv_model): GraphSAGEModel(\n",
      "    (layers): ModuleList(\n",
      "      (0): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=14, out_features=14, bias=True)\n",
      "        (fc_neigh): Linear(in_features=14, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=14, out_features=32, bias=True)\n",
      "      )\n",
      "      (1): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=32, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=32, bias=True)\n",
      "      )\n",
      "      (2): SAGEConv(\n",
      "        (feat_drop): Dropout(p=0.0, inplace=False)\n",
      "        (fc_pool): Linear(in_features=32, out_features=32, bias=True)\n",
      "        (fc_neigh): Linear(in_features=32, out_features=2, bias=False)\n",
      "        (fc_self): Linear(in_features=32, out_features=2, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (loss_fcn): CrossEntropyLoss()\n",
      ")\n",
      "INFO:root:<generator object Module.parameters at 0x7fa002a356d0>\n",
      "INFO:root:Starting Model training\n",
      "INFO:root:Epoch 00000 | Time(s) 0.0306 | Training Loss 1.0220 | Training F1 0.7021 | Validation Accuracy 0.5456 | Validation F1 0.7046\n",
      "INFO:root:Epoch 00001 | Time(s) 0.0284 | Training Loss 3.0074 | Training F1 0.0000 | Validation Accuracy 0.4582 | Validation F1 0.0000\n",
      "INFO:root:Epoch 00002 | Time(s) 0.0274 | Training Loss 3.8020 | Training F1 0.0462 | Validation Accuracy 0.4658 | Validation F1 0.0410\n",
      "INFO:root:Epoch 00003 | Time(s) 0.0268 | Training Loss 1.6352 | Training F1 0.7249 | Validation Accuracy 0.5951 | Validation F1 0.7266\n",
      "INFO:root:Epoch 00004 | Time(s) 0.0265 | Training Loss 1.1646 | Training F1 0.7036 | Validation Accuracy 0.5456 | Validation F1 0.7046\n",
      "INFO:root:Epoch 00005 | Time(s) 0.0263 | Training Loss 1.7873 | Training F1 0.7072 | Validation Accuracy 0.5475 | Validation F1 0.7054\n",
      "INFO:root:Epoch 00006 | Time(s) 0.0262 | Training Loss 1.3513 | Training F1 0.7403 | Validation Accuracy 0.6445 | Validation F1 0.7317\n",
      "INFO:root:Epoch 00007 | Time(s) 0.0260 | Training Loss 0.6289 | Training F1 0.0753 | Validation Accuracy 0.4772 | Validation F1 0.0803\n",
      "INFO:root:Epoch 00008 | Time(s) 0.0261 | Training Loss 1.0993 | Training F1 0.0802 | Validation Accuracy 0.4696 | Validation F1 0.0478\n",
      "INFO:root:Epoch 00009 | Time(s) 0.0260 | Training Loss 1.0010 | Training F1 0.4564 | Validation Accuracy 0.5875 | Validation F1 0.4746\n",
      "INFO:root:Epoch 00010 | Time(s) 0.0260 | Training Loss 0.6884 | Training F1 0.7247 | Validation Accuracy 0.6502 | Validation F1 0.7204\n",
      "INFO:root:Epoch 00011 | Time(s) 0.0259 | Training Loss 0.6271 | Training F1 0.7276 | Validation Accuracy 0.5875 | Validation F1 0.7178\n",
      "INFO:root:Epoch 00012 | Time(s) 0.0258 | Training Loss 0.7001 | Training F1 0.7204 | Validation Accuracy 0.5760 | Validation F1 0.7145\n",
      "INFO:root:Epoch 00013 | Time(s) 0.0258 | Training Loss 0.7350 | Training F1 0.7238 | Validation Accuracy 0.5875 | Validation F1 0.7178\n",
      "INFO:root:Epoch 00014 | Time(s) 0.0258 | Training Loss 0.6959 | Training F1 0.7349 | Validation Accuracy 0.6616 | Validation F1 0.7500\n",
      "INFO:root:Epoch 00015 | Time(s) 0.0257 | Training Loss 0.6344 | Training F1 0.7167 | Validation Accuracy 0.6863 | Validation F1 0.7300\n",
      "INFO:root:Epoch 00016 | Time(s) 0.0257 | Training Loss 0.6091 | Training F1 0.6312 | Validation Accuracy 0.6521 | Validation F1 0.6460\n",
      "INFO:root:Epoch 00017 | Time(s) 0.0257 | Training Loss 0.6247 | Training F1 0.5593 | Validation Accuracy 0.6293 | Validation F1 0.5842\n",
      "INFO:root:Epoch 00018 | Time(s) 0.0257 | Training Loss 0.6409 | Training F1 0.5691 | Validation Accuracy 0.6464 | Validation F1 0.6043\n",
      "INFO:root:Epoch 00019 | Time(s) 0.0256 | Training Loss 0.6329 | Training F1 0.6381 | Validation Accuracy 0.6844 | Validation F1 0.6732\n",
      "INFO:root:Epoch 00020 | Time(s) 0.0257 | Training Loss 0.6083 | Training F1 0.7135 | Validation Accuracy 0.7243 | Validation F1 0.7487\n",
      "INFO:root:Epoch 00021 | Time(s) 0.0352 | Training Loss 0.5835 | Training F1 0.7486 | Validation Accuracy 0.7471 | Validation F1 0.7865\n",
      "INFO:root:Epoch 00022 | Time(s) 0.0362 | Training Loss 0.5723 | Training F1 0.7561 | Validation Accuracy 0.7414 | Validation F1 0.7895\n",
      "INFO:root:Epoch 00023 | Time(s) 0.0364 | Training Loss 0.5690 | Training F1 0.7611 | Validation Accuracy 0.7414 | Validation F1 0.7875\n",
      "INFO:root:Epoch 00024 | Time(s) 0.0359 | Training Loss 0.5588 | Training F1 0.7576 | Validation Accuracy 0.7490 | Validation F1 0.7829\n",
      "INFO:root:Epoch 00025 | Time(s) 0.0355 | Training Loss 0.5427 | Training F1 0.7127 | Validation Accuracy 0.7357 | Validation F1 0.7421\n",
      "INFO:root:Epoch 00026 | Time(s) 0.0352 | Training Loss 0.5487 | Training F1 0.7338 | Validation Accuracy 0.7567 | Validation F1 0.7714\n",
      "INFO:root:Epoch 00027 | Time(s) 0.0349 | Training Loss 0.5370 | Training F1 0.7635 | Validation Accuracy 0.7624 | Validation F1 0.7920\n",
      "INFO:root:Epoch 00028 | Time(s) 0.0348 | Training Loss 0.5275 | Training F1 0.7697 | Validation Accuracy 0.7757 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00029 | Time(s) 0.0344 | Training Loss 0.5312 | Training F1 0.7728 | Validation Accuracy 0.7795 | Validation F1 0.8135\n",
      "INFO:root:Epoch 00030 | Time(s) 0.0342 | Training Loss 0.5240 | Training F1 0.7658 | Validation Accuracy 0.7586 | Validation F1 0.7799\n",
      "INFO:root:Epoch 00031 | Time(s) 0.0339 | Training Loss 0.5137 | Training F1 0.7567 | Validation Accuracy 0.7624 | Validation F1 0.7723\n",
      "INFO:root:Epoch 00032 | Time(s) 0.0336 | Training Loss 0.5150 | Training F1 0.7679 | Validation Accuracy 0.7605 | Validation F1 0.7774\n",
      "INFO:root:Epoch 00033 | Time(s) 0.0334 | Training Loss 0.5045 | Training F1 0.7824 | Validation Accuracy 0.7700 | Validation F1 0.7966\n",
      "INFO:root:Epoch 00034 | Time(s) 0.0332 | Training Loss 0.5004 | Training F1 0.7849 | Validation Accuracy 0.7738 | Validation F1 0.8020\n",
      "INFO:root:Epoch 00035 | Time(s) 0.0330 | Training Loss 0.4968 | Training F1 0.7859 | Validation Accuracy 0.7890 | Validation F1 0.8096\n",
      "INFO:root:Epoch 00036 | Time(s) 0.0328 | Training Loss 0.4900 | Training F1 0.7787 | Validation Accuracy 0.7833 | Validation F1 0.7950\n",
      "INFO:root:Epoch 00037 | Time(s) 0.0326 | Training Loss 0.4863 | Training F1 0.7807 | Validation Accuracy 0.7833 | Validation F1 0.7950\n",
      "INFO:root:Epoch 00038 | Time(s) 0.0324 | Training Loss 0.4819 | Training F1 0.7926 | Validation Accuracy 0.7852 | Validation F1 0.8042\n",
      "INFO:root:Epoch 00039 | Time(s) 0.0322 | Training Loss 0.4751 | Training F1 0.8017 | Validation Accuracy 0.7909 | Validation F1 0.8142\n",
      "INFO:root:Epoch 00040 | Time(s) 0.0321 | Training Loss 0.4718 | Training F1 0.7948 | Validation Accuracy 0.7909 | Validation F1 0.8110\n",
      "INFO:root:Epoch 00041 | Time(s) 0.0319 | Training Loss 0.4675 | Training F1 0.7916 | Validation Accuracy 0.7985 | Validation F1 0.8140\n",
      "INFO:root:Epoch 00042 | Time(s) 0.0318 | Training Loss 0.4644 | Training F1 0.8043 | Validation Accuracy 0.7947 | Validation F1 0.8163\n",
      "INFO:root:Epoch 00043 | Time(s) 0.0316 | Training Loss 0.4592 | Training F1 0.8084 | Validation Accuracy 0.7985 | Validation F1 0.8197\n",
      "INFO:root:Epoch 00044 | Time(s) 0.0315 | Training Loss 0.4546 | Training F1 0.8051 | Validation Accuracy 0.8099 | Validation F1 0.8258\n",
      "INFO:root:Epoch 00045 | Time(s) 0.0314 | Training Loss 0.4498 | Training F1 0.8061 | Validation Accuracy 0.8042 | Validation F1 0.8215\n",
      "INFO:root:Epoch 00046 | Time(s) 0.0313 | Training Loss 0.4450 | Training F1 0.8115 | Validation Accuracy 0.8023 | Validation F1 0.8225\n",
      "INFO:root:Epoch 00047 | Time(s) 0.0311 | Training Loss 0.4413 | Training F1 0.8079 | Validation Accuracy 0.8099 | Validation F1 0.8252\n",
      "INFO:root:Epoch 00048 | Time(s) 0.0310 | Training Loss 0.4370 | Training F1 0.8123 | Validation Accuracy 0.8061 | Validation F1 0.8229\n",
      "INFO:root:Epoch 00049 | Time(s) 0.0309 | Training Loss 0.4333 | Training F1 0.8141 | Validation Accuracy 0.8118 | Validation F1 0.8296\n",
      "INFO:root:Epoch 00050 | Time(s) 0.0308 | Training Loss 0.4306 | Training F1 0.8148 | Validation Accuracy 0.8213 | Validation F1 0.8333\n",
      "INFO:root:Epoch 00051 | Time(s) 0.0308 | Training Loss 0.4279 | Training F1 0.8138 | Validation Accuracy 0.8099 | Validation F1 0.8294\n",
      "INFO:root:Epoch 00052 | Time(s) 0.0308 | Training Loss 0.4264 | Training F1 0.8121 | Validation Accuracy 0.8213 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00053 | Time(s) 0.0307 | Training Loss 0.4249 | Training F1 0.8203 | Validation Accuracy 0.8023 | Validation F1 0.8237\n",
      "INFO:root:Epoch 00054 | Time(s) 0.0311 | Training Loss 0.4202 | Training F1 0.8193 | Validation Accuracy 0.8118 | Validation F1 0.8296\n",
      "INFO:root:Epoch 00055 | Time(s) 0.0312 | Training Loss 0.4157 | Training F1 0.8163 | Validation Accuracy 0.8327 | Validation F1 0.8429\n",
      "INFO:root:Epoch 00056 | Time(s) 0.0315 | Training Loss 0.4153 | Training F1 0.8228 | Validation Accuracy 0.8042 | Validation F1 0.8309\n",
      "INFO:root:Epoch 00057 | Time(s) 0.0314 | Training Loss 0.4209 | Training F1 0.7991 | Validation Accuracy 0.8099 | Validation F1 0.8127\n",
      "INFO:root:Epoch 00058 | Time(s) 0.0314 | Training Loss 0.4226 | Training F1 0.8220 | Validation Accuracy 0.8156 | Validation F1 0.8342\n",
      "INFO:root:Epoch 00059 | Time(s) 0.0313 | Training Loss 0.4063 | Training F1 0.8280 | Validation Accuracy 0.8061 | Validation F1 0.8311\n",
      "INFO:root:Epoch 00060 | Time(s) 0.0312 | Training Loss 0.4137 | Training F1 0.8171 | Validation Accuracy 0.8251 | Validation F1 0.8327\n",
      "INFO:root:Epoch 00061 | Time(s) 0.0312 | Training Loss 0.4071 | Training F1 0.8204 | Validation Accuracy 0.8289 | Validation F1 0.8370\n",
      "INFO:root:Epoch 00062 | Time(s) 0.0311 | Training Loss 0.4041 | Training F1 0.8266 | Validation Accuracy 0.8156 | Validation F1 0.8375\n",
      "INFO:root:Epoch 00063 | Time(s) 0.0311 | Training Loss 0.4044 | Training F1 0.8300 | Validation Accuracy 0.8194 | Validation F1 0.8376\n",
      "INFO:root:Epoch 00064 | Time(s) 0.0310 | Training Loss 0.3985 | Training F1 0.8169 | Validation Accuracy 0.8156 | Validation F1 0.8200\n",
      "INFO:root:Epoch 00065 | Time(s) 0.0309 | Training Loss 0.4036 | Training F1 0.8353 | Validation Accuracy 0.8194 | Validation F1 0.8365\n",
      "INFO:root:Epoch 00066 | Time(s) 0.0308 | Training Loss 0.3939 | Training F1 0.8342 | Validation Accuracy 0.8213 | Validation F1 0.8428\n",
      "INFO:root:Epoch 00067 | Time(s) 0.0307 | Training Loss 0.3952 | Training F1 0.8327 | Validation Accuracy 0.8175 | Validation F1 0.8280\n",
      "INFO:root:Epoch 00068 | Time(s) 0.0307 | Training Loss 0.3907 | Training F1 0.8314 | Validation Accuracy 0.8156 | Validation F1 0.8265\n",
      "INFO:root:Epoch 00069 | Time(s) 0.0306 | Training Loss 0.3891 | Training F1 0.8362 | Validation Accuracy 0.8194 | Validation F1 0.8403\n",
      "INFO:root:Epoch 00070 | Time(s) 0.0305 | Training Loss 0.3884 | Training F1 0.8397 | Validation Accuracy 0.8327 | Validation F1 0.8483\n",
      "INFO:root:Epoch 00071 | Time(s) 0.0305 | Training Loss 0.3836 | Training F1 0.8318 | Validation Accuracy 0.8175 | Validation F1 0.8267\n",
      "INFO:root:Epoch 00072 | Time(s) 0.0304 | Training Loss 0.3846 | Training F1 0.8418 | Validation Accuracy 0.8365 | Validation F1 0.8497\n",
      "INFO:root:Epoch 00073 | Time(s) 0.0303 | Training Loss 0.3795 | Training F1 0.8407 | Validation Accuracy 0.8327 | Validation F1 0.8503\n",
      "INFO:root:Epoch 00074 | Time(s) 0.0302 | Training Loss 0.3801 | Training F1 0.8401 | Validation Accuracy 0.8251 | Validation F1 0.8357\n",
      "INFO:root:Epoch 00075 | Time(s) 0.0302 | Training Loss 0.3765 | Training F1 0.8430 | Validation Accuracy 0.8270 | Validation F1 0.8372\n",
      "INFO:root:Epoch 00076 | Time(s) 0.0301 | Training Loss 0.3745 | Training F1 0.8460 | Validation Accuracy 0.8289 | Validation F1 0.8475\n",
      "INFO:root:Epoch 00077 | Time(s) 0.0301 | Training Loss 0.3739 | Training F1 0.8479 | Validation Accuracy 0.8384 | Validation F1 0.8511\n",
      "INFO:root:Epoch 00078 | Time(s) 0.0300 | Training Loss 0.3699 | Training F1 0.8448 | Validation Accuracy 0.8270 | Validation F1 0.8378\n",
      "INFO:root:Epoch 00079 | Time(s) 0.0299 | Training Loss 0.3691 | Training F1 0.8501 | Validation Accuracy 0.8327 | Validation F1 0.8503\n",
      "INFO:root:Epoch 00080 | Time(s) 0.0299 | Training Loss 0.3678 | Training F1 0.8507 | Validation Accuracy 0.8346 | Validation F1 0.8476\n",
      "INFO:root:Epoch 00081 | Time(s) 0.0298 | Training Loss 0.3639 | Training F1 0.8509 | Validation Accuracy 0.8308 | Validation F1 0.8425\n",
      "INFO:root:Epoch 00082 | Time(s) 0.0298 | Training Loss 0.3629 | Training F1 0.8526 | Validation Accuracy 0.8308 | Validation F1 0.8489\n",
      "INFO:root:Epoch 00083 | Time(s) 0.0297 | Training Loss 0.3619 | Training F1 0.8522 | Validation Accuracy 0.8365 | Validation F1 0.8491\n",
      "INFO:root:Epoch 00084 | Time(s) 0.0297 | Training Loss 0.3582 | Training F1 0.8507 | Validation Accuracy 0.8327 | Validation F1 0.8451\n",
      "INFO:root:Epoch 00085 | Time(s) 0.0296 | Training Loss 0.3568 | Training F1 0.8564 | Validation Accuracy 0.8327 | Validation F1 0.8503\n",
      "INFO:root:Epoch 00086 | Time(s) 0.0296 | Training Loss 0.3558 | Training F1 0.8538 | Validation Accuracy 0.8365 | Validation F1 0.8497\n",
      "INFO:root:Epoch 00087 | Time(s) 0.0295 | Training Loss 0.3533 | Training F1 0.8563 | Validation Accuracy 0.8346 | Validation F1 0.8497\n",
      "INFO:root:Epoch 00088 | Time(s) 0.0295 | Training Loss 0.3508 | Training F1 0.8600 | Validation Accuracy 0.8346 | Validation F1 0.8513\n",
      "INFO:root:Epoch 00089 | Time(s) 0.0294 | Training Loss 0.3496 | Training F1 0.8536 | Validation Accuracy 0.8308 | Validation F1 0.8430\n",
      "INFO:root:Epoch 00090 | Time(s) 0.0294 | Training Loss 0.3486 | Training F1 0.8651 | Validation Accuracy 0.8308 | Validation F1 0.8479\n",
      "INFO:root:Epoch 00091 | Time(s) 0.0293 | Training Loss 0.3455 | Training F1 0.8606 | Validation Accuracy 0.8327 | Validation F1 0.8483\n",
      "INFO:root:Epoch 00092 | Time(s) 0.0293 | Training Loss 0.3431 | Training F1 0.8610 | Validation Accuracy 0.8384 | Validation F1 0.8527\n",
      "INFO:root:Epoch 00093 | Time(s) 0.0292 | Training Loss 0.3409 | Training F1 0.8664 | Validation Accuracy 0.8346 | Validation F1 0.8513\n",
      "INFO:root:Epoch 00094 | Time(s) 0.0292 | Training Loss 0.3410 | Training F1 0.8578 | Validation Accuracy 0.8327 | Validation F1 0.8434\n",
      "INFO:root:Epoch 00095 | Time(s) 0.0291 | Training Loss 0.3400 | Training F1 0.8700 | Validation Accuracy 0.8289 | Validation F1 0.8490\n",
      "INFO:root:Epoch 00096 | Time(s) 0.0291 | Training Loss 0.3395 | Training F1 0.8617 | Validation Accuracy 0.8327 | Validation F1 0.8456\n",
      "INFO:root:Epoch 00097 | Time(s) 0.0291 | Training Loss 0.3361 | Training F1 0.8722 | Validation Accuracy 0.8327 | Validation F1 0.8498\n",
      "INFO:root:Epoch 00098 | Time(s) 0.0290 | Training Loss 0.3320 | Training F1 0.8749 | Validation Accuracy 0.8327 | Validation F1 0.8483\n",
      "INFO:root:Epoch 00099 | Time(s) 0.0290 | Training Loss 0.3295 | Training F1 0.8706 | Validation Accuracy 0.8346 | Validation F1 0.8487\n",
      "INFO:root:Finished Model training\n",
      "INFO:root:Saving model\n",
      "INFO:root:Saving model predictions for test data\n"
     ]
    }
   ],
   "source": [
    "gcn_results_ind_all = pd.DataFrame(columns = [\"F1 Score\", \"ROC AUC\",\"Accuracy\",\"MCC\",\"Balanced Accuracy\",\"Precision\",\"Recall\"])\n",
    "\n",
    "for j in range(10):\n",
    "    \n",
    "    inductive_entry_train(\n",
    "        training_dir = \"gcn_processed_data\",\n",
    "        model_dir = \"gcn_results\",\n",
    "        output_dir = \"gcn_results\",\n",
    "        features_train = \"features_train.csv\",\n",
    "        features_test = \"features_test.csv\",\n",
    "        target_column = target_column,\n",
    "        source_destination_node_index = \"src_dst_df.csv\",\n",
    "        n_hidden = hyperparameters[\"n-hidden\"],\n",
    "        n_layers = hyperparameters[\"n-layers\"],\n",
    "        dropout = hyperparameters[\"dropout\"],\n",
    "        weight_decay = hyperparameters[\"weight-decay\"],\n",
    "        n_epochs = hyperparameters[\"n-epochs\"],\n",
    "        lr = hyperparameters[\"lr\"],\n",
    "        aggregator_type = hyperparameters[\"aggregator-type\"],\n",
    "        predictions_file_name = \"predictions_ind.csv\",\n",
    "        inductive = \"true\"\n",
    "    )\n",
    "    \n",
    "    gcn_output = pd.read_csv(os.path.join(\"gcn_results\", \"predictions_ind.csv\"))\n",
    "    \n",
    "    y_true, gcn_y_pred, gcn_y_pred_prob_class_1 = gcn_output[\"target\"], gcn_output[\"pred\"], gcn_output[\"pred_proba_class_1\"]\n",
    "    metrics_gcn_results = classification_report(y_true, gcn_y_pred, zero_division=1, output_dict=True)\n",
    "\n",
    "    gcn_results = pd.DataFrame(\n",
    "        {\n",
    "            \"F1 Score\": metrics_gcn_results[\"1\"][\"f1-score\"],\n",
    "            \"ROC AUC\": roc_auc_score(y_true, gcn_y_pred_prob_class_1),\n",
    "            \"Accuracy\": metrics_gcn_results[\"accuracy\"],\n",
    "            \"MCC\": matthews_corrcoef(y_true, gcn_y_pred),\n",
    "            \"Balanced Accuracy\": balanced_accuracy_score(y_true, gcn_y_pred),\n",
    "            \"Precision\": metrics_gcn_results[\"1\"][\"precision\"],\n",
    "            \"Recall\": metrics_gcn_results[\"1\"][\"recall\"],        \n",
    "        },\n",
    "        index=[\"GCN Inductive\"]\n",
    "    )\n",
    "    \n",
    "    gcn_results_ind_all = pd.concat([gcn_results_ind_all, gcn_results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8e68dc51-b3a8-46f4-9645-4dc817a443d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.801105</td>\n",
       "      <td>0.845282</td>\n",
       "      <td>0.780822</td>\n",
       "      <td>0.557566</td>\n",
       "      <td>0.777735</td>\n",
       "      <td>0.788043</td>\n",
       "      <td>0.814607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.784370</td>\n",
       "      <td>0.838143</td>\n",
       "      <td>0.773212</td>\n",
       "      <td>0.546793</td>\n",
       "      <td>0.774306</td>\n",
       "      <td>0.808955</td>\n",
       "      <td>0.761236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.786667</td>\n",
       "      <td>0.844750</td>\n",
       "      <td>0.756469</td>\n",
       "      <td>0.508198</td>\n",
       "      <td>0.749874</td>\n",
       "      <td>0.748731</td>\n",
       "      <td>0.828652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.785808</td>\n",
       "      <td>0.830201</td>\n",
       "      <td>0.751903</td>\n",
       "      <td>0.499756</td>\n",
       "      <td>0.743864</td>\n",
       "      <td>0.738272</td>\n",
       "      <td>0.839888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.785612</td>\n",
       "      <td>0.836547</td>\n",
       "      <td>0.773212</td>\n",
       "      <td>0.545942</td>\n",
       "      <td>0.773792</td>\n",
       "      <td>0.805310</td>\n",
       "      <td>0.766854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.778808</td>\n",
       "      <td>0.829622</td>\n",
       "      <td>0.745814</td>\n",
       "      <td>0.486671</td>\n",
       "      <td>0.738503</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.825843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.795610</td>\n",
       "      <td>0.840662</td>\n",
       "      <td>0.773212</td>\n",
       "      <td>0.541963</td>\n",
       "      <td>0.769430</td>\n",
       "      <td>0.777480</td>\n",
       "      <td>0.814607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.784713</td>\n",
       "      <td>0.840774</td>\n",
       "      <td>0.742770</td>\n",
       "      <td>0.484794</td>\n",
       "      <td>0.731588</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.865169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.787380</td>\n",
       "      <td>0.844824</td>\n",
       "      <td>0.764079</td>\n",
       "      <td>0.523464</td>\n",
       "      <td>0.760233</td>\n",
       "      <td>0.769437</td>\n",
       "      <td>0.806180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.792867</td>\n",
       "      <td>0.856891</td>\n",
       "      <td>0.770167</td>\n",
       "      <td>0.535797</td>\n",
       "      <td>0.766364</td>\n",
       "      <td>0.774799</td>\n",
       "      <td>0.811798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F1 Score   ROC AUC  Accuracy       MCC  Balanced Accuracy  Precision  \\\n",
       "0  0.801105  0.845282  0.780822  0.557566           0.777735   0.788043   \n",
       "1  0.784370  0.838143  0.773212  0.546793           0.774306   0.808955   \n",
       "2  0.786667  0.844750  0.756469  0.508198           0.749874   0.748731   \n",
       "3  0.785808  0.830201  0.751903  0.499756           0.743864   0.738272   \n",
       "4  0.785612  0.836547  0.773212  0.545942           0.773792   0.805310   \n",
       "5  0.778808  0.829622  0.745814  0.486671           0.738503   0.736842   \n",
       "6  0.795610  0.840662  0.773212  0.541963           0.769430   0.777480   \n",
       "7  0.784713  0.840774  0.742770  0.484794           0.731588   0.717949   \n",
       "8  0.787380  0.844824  0.764079  0.523464           0.760233   0.769437   \n",
       "9  0.792867  0.856891  0.770167  0.535797           0.766364   0.774799   \n",
       "\n",
       "     Recall  \n",
       "0  0.814607  \n",
       "1  0.761236  \n",
       "2  0.828652  \n",
       "3  0.839888  \n",
       "4  0.766854  \n",
       "5  0.825843  \n",
       "6  0.814607  \n",
       "7  0.865169  \n",
       "8  0.806180  \n",
       "9  0.811798  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcn_results_ind_all.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e5fabbab-d705-44c3-a967-30bd94717ea3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>AutoGluon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.788294</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.719251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>0.840770</td>\n",
       "      <td>0.007981</td>\n",
       "      <td>0.749468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.763166</td>\n",
       "      <td>0.013137</td>\n",
       "      <td>0.680365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.523094</td>\n",
       "      <td>0.026561</td>\n",
       "      <td>0.352409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.758569</td>\n",
       "      <td>0.016502</td>\n",
       "      <td>0.673490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.766582</td>\n",
       "      <td>0.030405</td>\n",
       "      <td>0.686224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.813483</td>\n",
       "      <td>0.031117</td>\n",
       "      <td>0.755618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Mean        SD  AutoGluon\n",
       "F1 Score           0.788294  0.006443   0.719251\n",
       "ROC AUC            0.840770  0.007981   0.749468\n",
       "Accuracy           0.763166  0.013137   0.680365\n",
       "MCC                0.523094  0.026561   0.352409\n",
       "Balanced Accuracy  0.758569  0.016502   0.673490\n",
       "Precision          0.766582  0.030405   0.686224\n",
       "Recall             0.813483  0.031117   0.755618"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mean = gcn_results_ind_all.mean(axis=0)\n",
    "res_sd = gcn_results_ind_all.std(axis=0)\n",
    "res = pd.concat([res_mean, res_sd], axis=1)\n",
    "res.columns = [\"Mean\", \"SD\"]\n",
    "res = pd.concat([res, ag_results.T], axis=1)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e7c928-ee53-4b9b-b521-f07c54934cf2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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