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"}}},{"cell_type":"markdown","source":"## Competition information:\n\n### Objective: \nThe goal of this competition is to understand the relationship between code and comments in Python notebooks. You are challenged to reconstruct the order of markdown cells in a given notebook based on the order of the code cells, demonstrating comprehension of which natural language references which code.\n\n### Data Description\nThe dataset for this competition comprises about 160,000 Jupyter notebooks published by the Kaggle community. Jupyter notebooks are the tool of choice for many data scientists for their ability to tell a narrative with both code and natural language. These two types of discourse are contained within cells of the notebook, and we refer to these cells as either code cells or markdown cells (markdown being the text formatting language used by Jupyter).\n\nYour task is to predict the correct ordering of the cells in a given notebook whose markdown cells have been shuffled.\n\nThe notebooks in this dataset have been selected and processed to ensure their suitability for the competition task. All notebooks:\n\nHave been published publicly on Kaggle under the Apache 2.0 open source license.\nRepresent the most recently published version of the notebook.\nContain at least one code cell and at least one markdown cell.\nHave code written in the Python language.\nHave had empty cells removed.\nThis is a code competition, in which you will submit a model or code that will be run against a future test set:\n\nThe first-stage test set contains notebooks from an approximately 90-day historical window of time.\nThe second-stage test set will contain a similar number of notebooks, collected from a future 90-day window of time. This is necessary to prevent models from looking up the order of existing public notebooks. The selection criteria for second-stage notebooks will be monitored for competition fairness purposes. For example, it will exclude competition participants' own notebooks.\n\n### Training Data\ntrain/ - A folder comprising about 140,000 JSON files with the filenames corresponding to the id field in the csv files. Each file contains the code and markdown cells of a Kaggle notebook. The code cells are in their original (correct) order. The markdown cells have been shuffled and placed after the code cells.\n>- train_orders.csv - Gives the correct order of the cells for each notebook in the train/ folder.\n>>- id - The notebook in file {id}.json.\n>>- cell_order - A space delimited list of the correct cell ordering given in terms of the order in {id}.json.\n>- train_ancestors.csv - On Kaggle, a user may \"fork\" (that is, copy) the notebook of another user to create their own version. This file contains the forking history of notebooks in the training set. Note: There is no corresponding file for the test set.\n>>- ancestor_id - Identifies sets of notebooks that have a common origin or \"ancestor\". As no notebook in the test set has an ancestor in the training set, you may find this field to be of use as a grouping factor when constructing validation splits.\n>>- parent_id - Indicates that some version of the notebook id was forked from some version of the notebook parent_id. The notebook parent_id may or may not be present in the training data. (The parent may be missing because someone had forked a private notebook of their own, for instance.)\n\n### Example Test Data\nTo help you author submission code, we include a few example instances selected from the test set. When you submit your notebook for scoring, this example data will be replaced by the actual test data, including the sample_submission.csv file.\n\ntest/ - A few example notebooks from the test set. The actual test set comprises about 20,000 notebooks in a format similar to the training set notebooks. No notebook in the test set has an ancestor in the training set.\nsample_submission.csv - A sample submission file in the correct format. \n\n\n### Evaluation metric:\nPredictions are evaluated by the Kendall tau correlation between predicted cell orders and ground truth cell orders accumulated across the entire collection of test set notebooks.\n\n![image.png](attachment:3a368080-1808-49aa-8e2f-699b50cf305d.png)","metadata":{},"attachments":{"3a368080-1808-49aa-8e2f-699b50cf305d.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAO8AAABOCAIAAABHUtalAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAAASdEVYdFNvZnR3YXJlAEdyZWVuc2hvdF5VCAUAAAqPSURBVHhe7Z19TE5/H8dLKpSs1ig1cou5Kj15SEnydCs3wmSxPKTS0jxE89RY1ohhZlprK1th1tAMLUxUiC0qPUhy3+UhhFBSVKr7vc73vtbdw1Wuc86v65zf5/XX+XzPub4n13md7/l8z3XOh1Y7QcgFspmQD2QzIR/IZkI+kM2EfCCbCflANhPygWwm5APZTMgHspmQD2QzIR/IZkI+kM2EfCCbCflANhPygWwm5APZrKHU1NT4+PhMmDDBycnpn//Pgg7mz58/e/ZsR0fHiRMnYjNw9epV9uH+0dLSkpmZuWrVKoVCgU7Cw8Pz8/MXL17MVksQsllDaW5uPnDggJaW1ujRo4uKiv7TiX938PLly5KSknv37sXFxTk4OGDL5cuX41Ps8/3gwoULtra22dnZVVVV6HDr1q3Y16ZNm9hqCUI2azRz587V1tbGIP3z50/W1BPfv38PDAzE+ArXWVNfVFRUmJqaZmVlsbjj/Bk+fHhKSgqLJQjZrNFgAIaj+vr6iYmJrKkX4CJShYsXL7K4L0JCQqytrTEqs7gDDw+PgoICFkgQslnTSUpKGjx4sImJyevXr1lTL2C47VN6JZ6enubm5shhWNwBzoePHz+yQIKQzZrO79+/d+/ejXwDE77379+zVt6sXLkSqbaNjc3atWsrKyuxF7ZCypDNEqC2ttbd3R1CR0REsCbe5OTkYGyG0ByY/2E6yNZJFrJZGjx48GDYsGGGhobXrl1jTbwpLS1FZmJhYcEJbWZm1tTUxNZJE7JZMqSmpmJ41tXVffHiBWsSgtbW1urqaoVCAaHv3LnDNcbHxyMD4ZYlBNksGZqbmwMCAuAc0ui2tjbW+uc0NDRs27atSw84VdCzMtkoKSkpLi7mliUE2SwlMjIy9PT0nj17xmK1uHLlyty5c7vYnJCQYGJiwv340tjYCOO5dmlBNkuG9+/fL1myBAk0i/uit2whNDTUzs7u8+fPLO7A0dHx5MmTWKiqqjp+/HhQUFCXW9GSgGyWBr9+/XJxcUlOTmZxP+gxW8A8b+bMmZaWluHh4QUFBZWVlcjC/f39cZ58/foVG8TFxaFlzJgxT5484T4iIchmCfDz508MqPv27VN9VxhrkSRwy71lC2/fvoW7paWlu3btGj9+vJWVlaura2xsLKcyKC8vT0lJcXBw+PLlC9ciIchmCXDixIktW7a0tLSwuBdyc3MPHTqEBT7ZAvaycOHCAwcOKE8MCcHL5kePHrm5udnY2IwbN27y5Mm4hJ07d45bFRkZ+Y8OnJyc9u7dyzVKjrKyMiMjIxYMBBhuL1y44OHh8eHDB9bUCxhKly9ffvv2bSzzyRaQe2DMxgd37tzJmqQDL5t//PiB4+3p6amjo3Pq1KnXr1/X1dVxq54+fWphYZGYmIivtaamhmsUhPr6ehza/Pz8iIgIXC5ZqwhglMJsSUtrIC9fsGrevHl9PjuBLxlDycSJE7lnOfhkCzhYM2bMCAwM7PIIhyTge6jwfdna2g4dOrSwsJA1tbdnZmauWLHi4cOHLBaUPXv2mJubOzs7wzNcf1mrCERHRw8ZMmQAbcZIMWnSpKNHj+L77A6G4fT09NOnT69cuVJfXx9/J8ZmLhuRdLbAB76HCrMKPT09U1NTboLS2tqK7zc4OLi2tpbbQCSQEYpqM+b+fn5+SJ8GymZMy2Ckrq6ugYGBYU9gBMFaJTjxkJNwn5V0tsAHvocqISEBx3vDhg1YhtD4+vbv38+tEhVRbcbYhkHx0qVLSPoHymY+SDpb4APfQ7Vs2TIc7xs3brx58wYDBnLoX79+sXViIqrNly9fDgoKwoJEbf7bwutQYRaICxwkxpxj8+bNSDkQ/jXjgXg2I1maOnUqd7OWbJYWvA7VrVu3cLCRtPn4+DT/763M1atXs9W98/nz5/Xr1/9LJf7+/mzrnhDJZuQYoaGhZ86c4UKyWVrwOlRIkbW1tfft28c9wlJXV2diYjJo0KAXgj6y2CN92gwjV61a5d4LS5Ys6fGzeXl53t7e3J0B0KPNiwmhYd8sb9S3GbN+OIGZdXp6Omtqb1+3bh0Of2RkJJ9HFvuDSGNzUlLS7NmzuYIVgLtDh4UjR46wLQgNRn2bq6urbW1tTU1NO7/1npqaisTDxcVF+TNKjyDTCAgIwMmgggHJNBobG2s6MWrUKOwFC/X19WwLQoNRx2ZciHGAs7OzMXRZWVlBTeUbOBUVFZgUItm4efPmt2/fMKPi2gUE+8Kp8uzZM3gWHByMZUxG2TrhqK2tffXqFc5V7KWyshL/XraC0GDUsTkrK0uhUHDvk0FoGxsbZbLh7OwMldFubGy8aNGiT58+ce0CkpycjGvC2LFjsZfhw4dj73PmzGHrhCM8PNza2lpHRwd7GT9+vICvlxLioX6m8TcHo7XYdeLUoKioKDo6mgUaBi6hmFZh4FP9XCsfyGY1Ud6RFK9O3J9SVlaGS1afT9sNIO/evXNzcxPpAR5ANvNCvDpxarBx48b+v2clHqofcrx//z4yN8ypWCwoZDMvxKsT96fk5uZi0sKCAUX1Q44NDQ3u7u7bt28X4x4u2cwXkerE/SlIY3x9fVmgAai4hYoBW6FQVFdXs1g4yGa+YE4jYJ24pqammJgYW1tbLy8vHO/Tp0+7urpiBunt7Y2kk23UE/b29p2L1ardj1CosPnLly8GBgY5OTksFg6yWQAErBNXXFwcFhZWWFhoaGjo4uISHx//8ePHjIyMESNGIFFhG3UDV20dHZ3Or7Wq14+AqP55a+TIkSdOnGCBcJDNwiBUnbjY2Ni0tLTnz58PGjQoJCSEa0RObGxsrKKUFkyFOsrfsIB6/QiIapvt7OzEKOJPNguGIHXiioqKPn36hHFLT09PWRUAc0dMNI8dO8aFoEvll/LycqjT+T6uev0IiGqbp02bhkSfBcJBNgtGs0B14sCsWbOmTJnCgvb2HTt2oNv8/HwWd6v8wv3O3/05gj/tpzPqPYSoRLXN+MMWLFjAAuEgm4VEkDpxdXV16CQ4OJjF7e0KhWLq1KlIJG7duoWwe+UXZBRQp8tvbGr0IyCqbUYqj/OBBcJBNguGUHXi0tPTkexiLRc+fvwYCUxSUlJZWZmvry8s6V75hVOnc94M1OhHQLg/qTebJ0+e3Pk0EwqyWRiEqhMHoqKijIyMlP9b1NmzZ5GLFxQUHD58OCUlpcfKLxiVYWqXegNq9CMIOKn6fMjRzMwsJiaGBcJBNguAgHXiALqyt7dXFnaBDW5ubnZ2dgcPHsRHeqv8giwiLS2NBR2o1w9/+nzIkbvfLMaP8GSzAGhCnbilS5f6+fmxoB/01s9fQGRk5KRJk8R4Oops5gWGWw2pE3f9+vXp06ezoB8MVAUZpGSenp5hYWFivMlBNvMCNmhOnTiMtZ3rp6lGRT+i8ujRI0tLS5He5SGb1UfT6sTl5eVhuFX+VqKBQGIvL6/79++zWGjIZjXRwDpxbW1td+/ejYqKYrGGgannmjVrzp8/3+cEQ23I5gFgoK7ysodsJuQD2UzIB7KZkA9kMyEfyGZCPpDNhHwgmwn5QDYT8oFsJuQD2UzIB7KZkA9kMyEfyGZCPpDNhFxob/8ve7w/B70qB0MAAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"## Import packages","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport random\nimport json\nfrom termcolor import colored\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nimport plotly.express as px\nfrom plotly.offline import init_notebook_mode, iplot\nfrom plotly.graph_objs import *\ninit_notebook_mode(connected=True)         # initiate notebook for offline plot\nimport matplotlib.pyplot as plt\nimport plotly.graph_objs as go\nfrom tqdm import tqdm\nfrom bisect import bisect\nfrom pathlib import Path\nfrom sklearn.model_selection import GroupShuffleSplit\nfrom sklearn.metrics import mean_squared_error\nfrom tqdm import tqdm\nimport sys, os\nfrom transformers import AutoTokenizer, AutoModel\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\n\npd.options.display.width = 180\npd.options.display.max_colwidth = 120\n\nSEED = 42","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:51:36.219187Z","iopub.execute_input":"2022-06-26T21:51:36.219536Z","iopub.status.idle":"2022-06-26T21:51:46.185826Z","shell.execute_reply.started":"2022-06-26T21:51:36.219507Z","shell.execute_reply":"2022-06-26T21:51:46.184975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load data","metadata":{}},{"cell_type":"code","source":"train_orders = pd.read_csv('../input/AI4Code/train_orders.csv')\ntrain_ancestors = pd.read_csv('../input/AI4Code/train_ancestors.csv')\nsample_submission = pd.read_csv('../input/AI4Code/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:25.11965Z","iopub.execute_input":"2022-06-26T21:44:25.120303Z","iopub.status.idle":"2022-06-26T21:44:25.902036Z","shell.execute_reply.started":"2022-06-26T21:44:25.120265Z","shell.execute_reply":"2022-06-26T21:44:25.901205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## EDA","metadata":{}},{"cell_type":"markdown","source":"### Look at the data","metadata":{}},{"cell_type":"code","source":"print(train_orders.shape)\nassert train_orders.shape[0] == train_orders['id'].nunique()\ntrain_orders['# cells'] = train_orders['cell_order'].apply(lambda x: len(x.split(' ')))\ntrain_orders.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:25.903605Z","iopub.execute_input":"2022-06-26T21:44:25.903997Z","iopub.status.idle":"2022-06-26T21:44:26.284652Z","shell.execute_reply.started":"2022-06-26T21:44:25.903958Z","shell.execute_reply":"2022-06-26T21:44:26.283774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train_ancestors.shape)\nassert train_ancestors.shape[0] == train_ancestors['id'].nunique()\nprint('# Parent notebooks:',train_ancestors['parent_id'].nunique())\nprint('# Parent notebooks not in train data:',train_ancestors[~train_ancestors['parent_id'].isin(train_orders['id'].tolist())]['parent_id'].nunique())\nprint('# Ancestor notebooks:',train_ancestors['ancestor_id'].nunique())\nprint('# Notebooks with no ancestors:', train_ancestors['ancestor_id'].isnull().sum())\nprint('# Ancestor notebooks not in train data:',train_ancestors[~train_ancestors['ancestor_id'].isin(train_orders['id'].tolist())]['ancestor_id'].nunique())\nprint('Overlap between train_orders and train_ancestors datasets:',train_orders[train_orders['id'].isin(train_ancestors['id'].tolist())].shape[0]/train_orders.shape[0] * 100,'%')\ntrain_ancestors.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:26.285981Z","iopub.execute_input":"2022-06-26T21:44:26.286407Z","iopub.status.idle":"2022-06-26T21:44:26.5775Z","shell.execute_reply.started":"2022-06-26T21:44:26.28637Z","shell.execute_reply":"2022-06-26T21:44:26.576687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's denote a fork as \"->\". e.g. A -> B means that \"B is a fork of A\".\n\nLet's say that there were two chains of forks:\n>- A -> B -> C\n>- A -> D -> E\n\nThen C has parent B, and E has parent D (different parents!), but they share a common ancestor A which is where the parents were forked from. In this example, C and E would have the same ancestor.\n\nReference: https://www.kaggle.com/competitions/AI4Code/discussion/327905","metadata":{}},{"cell_type":"code","source":"print(sample_submission.shape)\nprint('\\n# Test data notebooks in train data:', sample_submission[sample_submission['id'].isin(train_orders['id'].tolist())].shape[0])\nsample_submission.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:26.579576Z","iopub.execute_input":"2022-06-26T21:44:26.580466Z","iopub.status.idle":"2022-06-26T21:44:26.617398Z","shell.execute_reply.started":"2022-06-26T21:44:26.580425Z","shell.execute_reply":"2022-06-26T21:44:26.616529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Top notebooks","metadata":{}},{"cell_type":"code","source":"print('\\nTop 10 ancestors with highest # latest notebooks\\n')\ntrain_ancestors.groupby(['ancestor_id']).agg({'id':'nunique'}).reset_index().sort_values(by = 'id', ascending = False).rename(columns = {'id':'# Notebooks linked'}).head(10).reset_index(drop = True)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:26.618854Z","iopub.execute_input":"2022-06-26T21:44:26.619248Z","iopub.status.idle":"2022-06-26T21:44:26.908106Z","shell.execute_reply.started":"2022-06-26T21:44:26.619211Z","shell.execute_reply":"2022-06-26T21:44:26.907156Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('\\nTop 10 notebooks with highest # cells\\n')\ntrain_orders.sort_values(by = '# cells', ascending = False).head(10).reset_index(drop = True)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:26.909447Z","iopub.execute_input":"2022-06-26T21:44:26.909887Z","iopub.status.idle":"2022-06-26T21:44:26.951484Z","shell.execute_reply.started":"2022-06-26T21:44:26.909848Z","shell.execute_reply":"2022-06-26T21:44:26.950714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Visualize notebook","metadata":{}},{"cell_type":"code","source":"def show_notebook(sample_id, num_cells_to_show):\n    print('\\n# Cells in notebook:', train_orders[train_orders['id']==sample_id]['# cells'].iloc[0])\n    json_data = json.load(open('../input/AI4Code/train//' + sample_id + '.json', \"r\"))\n    assert (train_orders[train_orders['id']==sample_id]['# cells'].iloc[0]==len(json_data['source'].keys())) and (train_orders[train_orders['id']==sample_id]['# cells'].iloc[0]==len(json_data['cell_type'].keys()))\n    cell_order = train_orders[train_orders['id']==sample_id]['cell_order'].iloc[0].split(' ')\n    full_text = [json_data['source'][x] for x in json_data['source'].keys()]\n    print('\\n# Characters in entire notebook:', len('\\n'.join(full_text)),'\\n')\n    print('# Space separated words in entire notebook:', len('\\n'.join(full_text).split(' ')),'\\n')\n    print(colored('---------------------------Notebook---------------------------', attrs=['bold']),'\\n')\n    for i in range(0, len(cell_order)):\n        key = cell_order[i]\n        color = 'green' if json_data['cell_type'][key] == 'code' else 'red'\n        print(colored(json_data['cell_type'][key] + ' cell:', color), json_data['source'][key].split('\\n'),'\\n')\n        if num_cells_to_show <= i:\n            break\n\nnum_cells_list = [x for x in range(1, 1005, 10)]          \nwidgets.interactive(show_notebook, sample_id = random.sample(train_orders['id'].tolist(),5000), num_cells_to_show = num_cells_list)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:26.952892Z","iopub.execute_input":"2022-06-26T21:44:26.953454Z","iopub.status.idle":"2022-06-26T21:44:27.622835Z","shell.execute_reply.started":"2022-06-26T21:44:26.953417Z","shell.execute_reply":"2022-06-26T21:44:27.621985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# For test data\nnum_cells_to_show = 50\nsample_id = '0009d135ece78d'\nsample_submission['# cells'] = sample_submission['cell_order'].apply(lambda x: len(x.split(' ')))\nprint('\\n# Cells in notebook:', sample_submission[sample_submission['id']==sample_id]['# cells'].iloc[0])\njson_data = json.load(open('../input/AI4Code/test//' + sample_id + '.json', \"r\"))\nassert (sample_submission[sample_submission['id']==sample_id]['# cells'].iloc[0]==len(json_data['source'].keys())) and (sample_submission[sample_submission['id']==sample_id]['# cells'].iloc[0]==len(json_data['cell_type'].keys()))\ncell_order = sample_submission[sample_submission['id']==sample_id]['cell_order'].iloc[0].split(' ')\nfull_text = [json_data['source'][x] for x in json_data['source'].keys()]\nprint('\\n# Characters in entire notebook:', len('\\n'.join(full_text)),'\\n')\nprint('# Space separated words in entire notebook:', len('\\n'.join(full_text).split(' ')),'\\n')\nprint(colored('---------------------------Notebook---------------------------', attrs=['bold']),'\\n')\nfor i in range(0, len(cell_order)):\n    key = cell_order[i]\n    color = 'green' if json_data['cell_type'][key] == 'code' else 'red'\n    print(colored(json_data['cell_type'][key] + ' cell:', color), json_data['source'][key].split('\\n'),'\\n')\n    if num_cells_to_show <= i:\n        break","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.625368Z","iopub.execute_input":"2022-06-26T21:44:27.625633Z","iopub.status.idle":"2022-06-26T21:44:27.648447Z","shell.execute_reply.started":"2022-06-26T21:44:27.625608Z","shell.execute_reply":"2022-06-26T21:44:27.647317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Understand the evaluation metric - Kendall Tau correlation","metadata":{}},{"cell_type":"markdown","source":"Kendall Tau correlation is a coefficient that represents the degree of concordance between 2 columns of ranked data.\n\nThe greater the number of \"inversions\", the smaller the coefficient. The Tau correlation coefficient returns a value of 0 to 1, where: 0 is no relationship, 1 is a perfect relationship. A quirk of this test is that it can also produce negative values (i.e. from -1 to 0). Unlike a linear graph, a negative relationship doesn’t mean much with ranked columns (other than you perhaps switched the columns around), so just remove the negative sign when you’re interpreting Tau.\n\nKendall rank correlation is used to test the similarities in the ordering of data when it is ranked by quantities. Other types of correlation coefficients use the observations as the basis of the correlation, Kendall’s correlation coefficient uses pairs of observations and determines the strength of association based on the patter on concordance and discordance between the pairs.\n>- Concordant: Ordered in the same way (consistency). A pair of observations is considered concordant if (x2 — x1) and (y2 — y1) have the same sign.\n>- Discordant: Ordered differently (inconsistency). A pair of observations is considered concordant if (x2 — x1) and (y2 — y1) have opposite signs.\n\nExample:\n\n>- Ground truth: ['1','2','3','4']\n>- Prediction: ['3','1','4','2']\n>- For every instance in prediction we find the number of inversions required to get a correct sorted list:\n>>- Assume an empty list \"sorted_so_far\"\n>>- For first entry '3' in prediction list, no inversion is required as list \"sorted_so_far\" is empty. Now \"sorted_so_far\" is filled with '3'.\n>>- For second entry '1', '1' is before '3' in ground truth list. Hence, '1' is to be put to the left of '3'. Number of inversions = 1 now. sorted_so_far = ['1','3']\n>>- For the third entry '4', '4' is to the right of '3' in the ground truth list and hence no numbersions required. As of now, sorted_so_far = ['1','3','4'].\n>>- For the fourth entry '2', '2' is to be put before '3' i.e.; 2 positions from the current position of '2' in the prediction list. Number of inversions = 1 + 2 = 3. As of now, sorted_so_far = ['1','2','3','4']\n>>- Maximum number of inversions possible = n*(n-1) = 4 * (4-1) = 12\n>>- Kendal Tau correation = 1 - 4 * Number of inversions/Max. Number of inversions = 1 - 4 * 3/12 = 0","metadata":{}},{"cell_type":"markdown","source":"References: \n>- https://www.kaggle.com/code/ryanholbrook/competition-metric-kendall-tau-correlation/notebook\n>- https://www.statisticshowto.com/kendalls-tau/","metadata":{}},{"cell_type":"code","source":"def count_inversions(a):\n    inversions = 0\n    sorted_so_far = []\n    for i, u in enumerate(a):  # O(N)\n        j = bisect(sorted_so_far, u)  # O(log N)\n        inversions += i - j\n        sorted_so_far.insert(j, u)  # O(N)\n    return inversions","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.651093Z","iopub.execute_input":"2022-06-26T21:44:27.651395Z","iopub.status.idle":"2022-06-26T21:44:27.658155Z","shell.execute_reply.started":"2022-06-26T21:44:27.651367Z","shell.execute_reply":"2022-06-26T21:44:27.655329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def kendall_tau(ground_truth, predictions):\n    total_inversions = 0  # total inversions in predicted ranks across all instances\n    total_2max = 0  # maximum possible inversions across all instances\n    for gt, pred in zip(ground_truth, predictions):\n        ranks = [gt.index(x) for x in pred]  # rank predicted order in terms of ground truth\n        total_inversions += count_inversions(ranks)\n        n = len(gt)\n        total_2max += n * (n - 1)\n    return 1 - 4 * total_inversions / total_2max","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.662036Z","iopub.execute_input":"2022-06-26T21:44:27.662909Z","iopub.status.idle":"2022-06-26T21:44:27.669314Z","shell.execute_reply.started":"2022-06-26T21:44:27.662867Z","shell.execute_reply":"2022-06-26T21:44:27.668403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_id = 'ba14b6fba142e9'\njson_data = json.load(open('../input/AI4Code/train//' + sample_id + '.json', \"r\"))\ncell_order = train_orders[train_orders['id']==sample_id]['cell_order'].iloc[0].split(' ')\ndummy_pred = cell_order.copy()\nrandom.Random(SEED).shuffle(dummy_pred)\ncell_order = [cell_order]\ndummy_pred = [dummy_pred]\nprint('\\nKendall Tau correlation - random shuffle of actual cell order:', kendall_tau(cell_order, dummy_pred))","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.67074Z","iopub.execute_input":"2022-06-26T21:44:27.671608Z","iopub.status.idle":"2022-06-26T21:44:27.720144Z","shell.execute_reply.started":"2022-06-26T21:44:27.671566Z","shell.execute_reply":"2022-06-26T21:44:27.718698Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Modelling","metadata":{}},{"cell_type":"markdown","source":"References: https://www.kaggle.com/code/aerdem4/ai4code-pytorch-distilbert-baseline","metadata":{}},{"cell_type":"code","source":"print('# Notebooks available for train dataset:',len(os.listdir('../input/AI4Code/train')))\nNUM_TRAIN = 10000\nprint('# Notebooks selected for modelling:', NUM_TRAIN)\n\nNVALID = 0.1  # size of validation set\nMAX_LEN = 512\nBS = 8\nNW = 8\nEPOCHS = 4","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.721432Z","iopub.execute_input":"2022-06-26T21:44:27.721868Z","iopub.status.idle":"2022-06-26T21:44:27.799196Z","shell.execute_reply.started":"2022-06-26T21:44:27.72183Z","shell.execute_reply":"2022-06-26T21:44:27.798187Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def read_notebook(path):\n    return (\n        pd.read_json(\n            path,\n            dtype={'cell_type': 'category', 'source': 'str'})\n        .assign(id=path.stem)\n        .rename_axis('cell_id')\n    )\n\ndata_dir = Path('../input/AI4Code')\npaths_train = list((data_dir / 'train').glob('*.json'))\npaths_train = random.sample(paths_train, NUM_TRAIN)\nnotebooks_train = [\n    read_notebook(path) for path in tqdm(paths_train, desc='Train NBs')\n]\ndf = (\n    pd.concat(notebooks_train)\n    .set_index('id', append=True)\n    .swaplevel()\n    .sort_index(level='id', sort_remaining=False)\n)\n\ndf","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:44:27.800953Z","iopub.execute_input":"2022-06-26T21:44:27.801343Z","iopub.status.idle":"2022-06-26T21:46:06.15355Z","shell.execute_reply.started":"2022-06-26T21:44:27.801304Z","shell.execute_reply":"2022-06-26T21:46:06.152778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get an example notebook\nnb_id = df.index.unique('id')[6]\nprint('Notebook:', nb_id)\n\nprint(\"The disordered notebook:\")\nnb = df.loc[nb_id, :]\ndisplay(nb)\nprint()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:06.156202Z","iopub.execute_input":"2022-06-26T21:46:06.156572Z","iopub.status.idle":"2022-06-26T21:46:06.178995Z","shell.execute_reply.started":"2022-06-26T21:46:06.156536Z","shell.execute_reply":"2022-06-26T21:46:06.177977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cell_order = train_orders[train_orders['id']==nb_id]['cell_order'].iloc[0].split()\n\nprint(\"The ordered notebook:\")\nnb.loc[cell_order, :]","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:06.180339Z","iopub.execute_input":"2022-06-26T21:46:06.181408Z","iopub.status.idle":"2022-06-26T21:46:06.216975Z","shell.execute_reply.started":"2022-06-26T21:46:06.181367Z","shell.execute_reply":"2022-06-26T21:46:06.216166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_ranks(base, derived):\n    return [base.index(d) for d in derived]\n\ncell_ranks = get_ranks(cell_order, list(nb.index))\nnb.insert(0, 'rank', cell_ranks)\n\nnb","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:06.218279Z","iopub.execute_input":"2022-06-26T21:46:06.21878Z","iopub.status.idle":"2022-06-26T21:46:06.234078Z","shell.execute_reply.started":"2022-06-26T21:46:06.218743Z","shell.execute_reply":"2022-06-26T21:46:06.233379Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_orders = pd.read_csv(\n    data_dir / 'train_orders.csv',\n    index_col='id',\n    squeeze=True,\n).str.split()  # Split the string representation of cell_ids into a list\n\ndf_orders_ = df_orders.to_frame().join(\n    df.reset_index('cell_id').groupby('id')['cell_id'].apply(list),\n    how='right',\n)\n\nranks = {}\nfor id_, cell_order, cell_id in df_orders_.itertuples():\n    ranks[id_] = {'cell_id': cell_id, 'rank': get_ranks(cell_order, cell_id)}\n\ndf_ranks = (\n    pd.DataFrame\n    .from_dict(ranks, orient='index')\n    .rename_axis('id')\n    .apply(pd.Series.explode)\n    .set_index('cell_id', append=True)\n)\n\ndf_ranks","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:06.235497Z","iopub.execute_input":"2022-06-26T21:46:06.236091Z","iopub.status.idle":"2022-06-26T21:46:10.745834Z","shell.execute_reply.started":"2022-06-26T21:46:06.236053Z","shell.execute_reply":"2022-06-26T21:46:10.744935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.reset_index().merge(df_ranks, on=[\"id\", \"cell_id\"]).merge(train_ancestors, on=[\"id\"])\ndf","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:10.747193Z","iopub.execute_input":"2022-06-26T21:46:10.74763Z","iopub.status.idle":"2022-06-26T21:46:11.408827Z","shell.execute_reply.started":"2022-06-26T21:46:10.747584Z","shell.execute_reply":"2022-06-26T21:46:11.4078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df[\"pct_rank\"] = df[\"rank\"] / df.groupby(\"id\")[\"cell_id\"].transform(\"count\")\n\ndf[\"pct_rank\"].hist(bins=10)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:11.410102Z","iopub.execute_input":"2022-06-26T21:46:11.410529Z","iopub.status.idle":"2022-06-26T21:46:11.951599Z","shell.execute_reply.started":"2022-06-26T21:46:11.410492Z","shell.execute_reply":"2022-06-26T21:46:11.950767Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:11.95276Z","iopub.execute_input":"2022-06-26T21:46:11.95312Z","iopub.status.idle":"2022-06-26T21:46:11.967478Z","shell.execute_reply.started":"2022-06-26T21:46:11.953083Z","shell.execute_reply":"2022-06-26T21:46:11.966631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"splitter = GroupShuffleSplit(n_splits=1, test_size=NVALID, random_state=0)\n\ntrain_ind, val_ind = next(splitter.split(df, groups=df[\"ancestor_id\"]))\n\ntrain_df = df.loc[train_ind].reset_index(drop=True)\nval_df = df.loc[val_ind].reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:11.969002Z","iopub.execute_input":"2022-06-26T21:46:11.969887Z","iopub.status.idle":"2022-06-26T21:46:13.042278Z","shell.execute_reply.started":"2022-06-26T21:46:11.96984Z","shell.execute_reply":"2022-06-26T21:46:13.041412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train_df.shape)\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.043827Z","iopub.execute_input":"2022-06-26T21:46:13.044256Z","iopub.status.idle":"2022-06-26T21:46:13.057804Z","shell.execute_reply.started":"2022-06-26T21:46:13.044215Z","shell.execute_reply":"2022-06-26T21:46:13.057031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(val_df.shape)\nval_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.059484Z","iopub.execute_input":"2022-06-26T21:46:13.060094Z","iopub.status.idle":"2022-06-26T21:46:13.076633Z","shell.execute_reply.started":"2022-06-26T21:46:13.060055Z","shell.execute_reply":"2022-06-26T21:46:13.075795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df_mark = train_df[train_df[\"cell_type\"] == \"markdown\"].reset_index(drop=True)\nval_df_mark = val_df[val_df[\"cell_type\"] == \"markdown\"].reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.080635Z","iopub.execute_input":"2022-06-26T21:46:13.081056Z","iopub.status.idle":"2022-06-26T21:46:13.195087Z","shell.execute_reply.started":"2022-06-26T21:46:13.080936Z","shell.execute_reply":"2022-06-26T21:46:13.194227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('# Mardown cells in train dataset:', train_df_mark.shape[0])\nprint('# Mardown cells in validation dataset:', val_df_mark.shape[0],'-->',val_df_mark.shape[0]/train_df_mark.shape[0]*100,'% of train')","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.196452Z","iopub.execute_input":"2022-06-26T21:46:13.196822Z","iopub.status.idle":"2022-06-26T21:46:13.202694Z","shell.execute_reply.started":"2022-06-26T21:46:13.196786Z","shell.execute_reply":"2022-06-26T21:46:13.201378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MarkdownModel(nn.Module):\n    def __init__(self):\n        super(MarkdownModel, self).__init__()\n        self.bert = AutoModel.from_pretrained(\"bert-base-uncased\")\n        self.top = nn.Linear(768, 1)\n        \n    def forward(self, ids, mask):\n        x = self.bert(ids, mask)[0]\n        x = self.top(x[:, 0, :])\n        return x","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.204194Z","iopub.execute_input":"2022-06-26T21:46:13.204556Z","iopub.status.idle":"2022-06-26T21:46:13.212589Z","shell.execute_reply.started":"2022-06-26T21:46:13.204519Z","shell.execute_reply":"2022-06-26T21:46:13.211817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MarkdownDataset(Dataset):\n    \n    def __init__(self, df, max_len):\n        super().__init__()\n        self.df = df.reset_index(drop=True)\n        self.max_len = max_len\n        self.tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n\n    def __getitem__(self, index):\n        row = self.df.iloc[index]\n        \n        inputs = self.tokenizer.encode_plus(\n            row.source,\n            None,\n            add_special_tokens=True,\n            max_length=self.max_len,\n            padding=\"max_length\",\n            return_token_type_ids=True,\n            truncation=True\n        )\n        ids = torch.LongTensor(inputs['input_ids'])\n        mask = torch.LongTensor(inputs['attention_mask'])\n\n        return ids, mask, torch.FloatTensor([row.pct_rank])\n\n    def __len__(self):\n        return self.df.shape[0]\n    \ntrain_ds = MarkdownDataset(train_df_mark, max_len=MAX_LEN)\nval_ds = MarkdownDataset(val_df_mark, max_len=MAX_LEN)\n\nval_ds[0]","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:13.213877Z","iopub.execute_input":"2022-06-26T21:46:13.214412Z","iopub.status.idle":"2022-06-26T21:46:19.295767Z","shell.execute_reply.started":"2022-06-26T21:46:13.214377Z","shell.execute_reply":"2022-06-26T21:46:19.294976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_df_mark.iloc[0]","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:19.297056Z","iopub.execute_input":"2022-06-26T21:46:19.297561Z","iopub.status.idle":"2022-06-26T21:46:19.305744Z","shell.execute_reply.started":"2022-06-26T21:46:19.297521Z","shell.execute_reply":"2022-06-26T21:46:19.304201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def adjust_lr(optimizer, epoch):\n    if epoch < 1:\n        lr = 5e-5\n    elif epoch < 2:\n        lr = 1e-3\n    elif epoch < 5:\n        lr = 1e-4\n    else:\n        lr = 1e-5\n\n    for p in optimizer.param_groups:\n        p['lr'] = lr\n    return lr\n    \ndef get_optimizer(net):\n    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=3e-4, betas=(0.9, 0.999),\n                                 eps=1e-08)\n    return optimizer","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:19.307876Z","iopub.execute_input":"2022-06-26T21:46:19.308791Z","iopub.status.idle":"2022-06-26T21:46:19.316832Z","shell.execute_reply.started":"2022-06-26T21:46:19.308749Z","shell.execute_reply":"2022-06-26T21:46:19.316172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_loader = DataLoader(train_ds, batch_size=BS, shuffle=True, num_workers=NW,\n                          pin_memory=False, drop_last=True)\nval_loader = DataLoader(val_ds, batch_size=BS, shuffle=False, num_workers=NW,\n                          pin_memory=False, drop_last=False)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:19.319851Z","iopub.execute_input":"2022-06-26T21:46:19.32012Z","iopub.status.idle":"2022-06-26T21:46:19.33915Z","shell.execute_reply.started":"2022-06-26T21:46:19.320097Z","shell.execute_reply":"2022-06-26T21:46:19.33824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def read_data(data):\n    return tuple(d.cuda() for d in data[:-1]), data[-1].cuda()\n\n\ndef validate(model, val_loader):\n    model.eval()\n    \n    tbar = tqdm(val_loader, file=sys.stdout)\n    \n    preds = []\n    labels = []\n\n    with torch.no_grad():\n        for idx, data in enumerate(tbar):\n            inputs, target = read_data(data)\n\n            pred = model(inputs[0], inputs[1])\n\n            preds.append(pred.detach().cpu().numpy().ravel())\n            labels.append(target.detach().cpu().numpy().ravel())\n    \n    return np.concatenate(labels), np.concatenate(preds)\n\ndef train(model, train_loader, val_loader, epochs):\n    np.random.seed(SEED)\n    \n    optimizer = get_optimizer(model)\n\n    criterion = torch.nn.MSELoss()\n    \n    best_valid_loss = np.inf\n    \n    for e in range(epochs):   \n        model.train()\n        tbar = tqdm(train_loader, file=sys.stdout)\n        \n        lr = adjust_lr(optimizer, e)\n        \n        loss_list = []\n        preds = []\n        labels = []\n\n        for idx, data in enumerate(tbar):\n            inputs, target = read_data(data)\n\n            optimizer.zero_grad()\n            pred = model(inputs[0], inputs[1])\n\n            loss = criterion(pred, target)\n            loss.backward()\n            optimizer.step()\n            \n            loss_list.append(loss.detach().cpu().item())\n            preds.append(pred.detach().cpu().numpy().ravel())\n            labels.append(target.detach().cpu().numpy().ravel())\n            \n            avg_loss = np.round(np.mean(loss_list), 4)\n\n            tbar.set_description(f\"Epoch {e+1} Loss: {avg_loss} lr: {lr}\")\n            \n        y_val, y_pred = validate(model, val_loader)\n        \n        print(\"Validation MSE:\", np.round(mean_squared_error(y_val, y_pred), 4))\n        if best_valid_loss > mean_squared_error(y_val, y_pred):\n            torch.save(model.state_dict(), f\"pytorch_best_model.bin\")\n        print()\n    return model, y_pred\n\nmodel = MarkdownModel()\nmodel = model.cuda()\nmodel, y_pred = train(model, train_loader, val_loader, epochs=EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2022-06-26T21:46:19.340758Z","iopub.execute_input":"2022-06-26T21:46:19.341401Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_df[\"pred\"] = val_df.groupby([\"id\", \"cell_type\"])[\"rank\"].rank(pct=True)\nval_df.loc[val_df[\"cell_type\"] == \"markdown\", \"pred\"] = y_pred","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_dummy = val_df.sort_values(\"pred\").groupby('id')['cell_id'].apply(list)\nkendall_tau(df_orders.loc[y_dummy.index], y_dummy)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Prediction","metadata":{}},{"cell_type":"code","source":"paths_test = list((data_dir / 'test').glob('*.json'))\nnotebooks_test = [\n    read_notebook(path) for path in tqdm(paths_test, desc='Test NBs')\n]\ntest_df = (\n    pd.concat(notebooks_test)\n    .set_index('id', append=True)\n    .swaplevel()\n    .sort_index(level='id', sort_remaining=False)\n).reset_index()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df[\"rank\"] = test_df.groupby([\"id\", \"cell_type\"]).cumcount()\ntest_df[\"pred\"] = test_df.groupby([\"id\", \"cell_type\"])[\"rank\"].rank(pct=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df[\"pct_rank\"] = 0\ntest_ds = MarkdownDataset(test_df[test_df[\"cell_type\"] == \"markdown\"].reset_index(drop=True), max_len=MAX_LEN)\ntest_loader = DataLoader(test_ds, batch_size=BS, shuffle=False, num_workers=NW,\n                          pin_memory=False, drop_last=False)\n\nlen(test_ds), test_ds[0]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"_, y_test = validate(model, test_loader)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.loc[test_df[\"cell_type\"] == \"markdown\", \"pred\"] = y_test","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df = test_df.sort_values(\"pred\").groupby(\"id\")[\"cell_id\"].apply(lambda x: \" \".join(x)).reset_index()\nsub_df.rename(columns={\"cell_id\": \"cell_order\"}, inplace=True)\nsub_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df.to_csv(\"submission.csv\", index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}