{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:20:51.182277Z","iopub.execute_input":"2023-02-13T04:20:51.182619Z","iopub.status.idle":"2023-02-13T04:20:51.335658Z","shell.execute_reply.started":"2023-02-13T04:20:51.182546Z","shell.execute_reply":"2023-02-13T04:20:51.334568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# libraries for plots\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.markers import MarkerStyle\nimport matplotlib.animation as animation\n\n# from fast_ml.model_development import train_valid_test_split\n\nimport time\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Video Display; IPython\nimport os\nimport cv2\nimport subprocess\nimport IPython\nfrom IPython.display import Video, display\n\n# Modeling\nfrom xgboost import XGBClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import GridSearchCV,RandomizedSearchCV,train_test_split\nfrom sklearn.metrics import accuracy_score,f1_score,roc_auc_score,roc_curve\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n\nfrom sklearn.metrics import matthews_corrcoef\n\nimport shap\n\nfrom time import time","metadata":{"execution":{"iopub.status.busy":"2023-02-13T05:44:38.585982Z","iopub.execute_input":"2023-02-13T05:44:38.586355Z","iopub.status.idle":"2023-02-13T05:44:38.594704Z","shell.execute_reply.started":"2023-02-13T05:44:38.586322Z","shell.execute_reply":"2023-02-13T05:44:38.593095Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### LOAD DATA","metadata":{}},{"cell_type":"code","source":"# Player tracking Data\ntracking_tr = pd.read_csv(\"/kaggle/input/nfl-player-contact-detection/train_player_tracking.csv\")\n\n# Contact label data \nlabels_tr = pd.read_csv(\"/kaggle/input/nfl-player-contact-detection/train_labels.csv\")\n\n# Helmet Baseline data \nbaseline_helmets_tr = pd.read_csv(\"/kaggle/input/nfl-player-contact-detection/train_baseline_helmets.csv\")\n\n# Video MetaData\nvideo_metadata_tr = pd.read_csv(\"/kaggle/input/nfl-player-contact-detection/train_video_metadata.csv\")\n","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:20:56.857426Z","iopub.execute_input":"2023-02-13T04:20:56.857995Z","iopub.status.idle":"2023-02-13T04:21:18.297068Z","shell.execute_reply.started":"2023-02-13T04:20:56.857948Z","shell.execute_reply":"2023-02-13T04:21:18.295999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## DATA DESCRIPTION","metadata":{}},{"cell_type":"code","source":"class data_info:\n    def __init__(self,df):\n        self.df = df\n\n    def info(self): \n        \n        # Counting no of rows \n        print(f'\\nTotal Rows : {self.df.shape[0]} \\n' + '--'*10 )\n      \n        # Counting no of columns\n        print(f'\\nTotal Columns : {self.df.shape[1]} \\n' + '--'*10)\n        \n        # Extracting column names\n        column_name =  self.df.columns \n        print(f'\\nColumn Names\\n' + '--'*10 +  f'\\n{column_name} \\n \\n')\n        \n        # Data type info\n        print(f'Data Summary\\n' + '--'*10)\n        data_summary = self.df.info() \n        \n        # Total null values by each categories\n        null_values = self.df.isnull().sum() \n        print(f'\\nNull values\\n' + '--'*10 + f'\\n{null_values} \\n \\n')\n\n        # Descriptive statistics\n        describe =  self.df.describe() \n        print(f'\\nDescriptive Statistics\\n' + '--'*10 +  f'\\n{describe} \\n \\n')\n        \n        # Top 5 rows\n        head =  self.df.head() \n        print(f'\\nTop 5 rows\\n' + '--'*10 +  f'\\n{head} \\n \\n')\n        \n        # Unique categories description\n    def uniqueCategory(self):      \n        \n        # Number of Unique values in each column\n        uniques_values = self.df.apply(lambda x : len(x.unique())) \n        print(f'Unique Values\\n' + '--'*10 +  f'\\n{uniques_values} \\n \\n')\n        \n        for i in self.df.columns: \n            if self.df[i].dtype == 'O':          # Columns having datatype object or categories in each column\n                unique_category = set(self.df.loc[: , i])      # Unique categories in each columns\n                \n                if len(unique_category)>=100:\n                    print(f'{i} -- {list(unique_category)[:10]}\\n')\n                    continue  #Taking only top 50 as more than 100 will messed up\n                else:\n                    print(f'\\n{i} -- {unique_category}\\n')\n                    self.df[i].value_counts().plot(kind='bar')\n                    plt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:23.050827Z","iopub.execute_input":"2023-02-13T04:21:23.051182Z","iopub.status.idle":"2023-02-13T04:21:23.062964Z","shell.execute_reply.started":"2023-02-13T04:21:23.051150Z","shell.execute_reply":"2023-02-13T04:21:23.062004Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tracking data\ntracking_tr_obj = data_info(tracking_tr)\n\ntracking_tr_obj.info()\ntracking_tr_obj.uniqueCategory()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:23.774207Z","iopub.execute_input":"2023-02-13T04:21:23.774987Z","iopub.status.idle":"2023-02-13T04:21:26.862456Z","shell.execute_reply.started":"2023-02-13T04:21:23.774946Z","shell.execute_reply":"2023-02-13T04:21:26.860739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# labels \nlabels_tr_obj = data_info(labels_tr)\n\nlabels_tr_obj.info()\nlabels_tr_obj.uniqueCategory()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:26.864587Z","iopub.execute_input":"2023-02-13T04:21:26.865216Z","iopub.status.idle":"2023-02-13T04:21:34.475045Z","shell.execute_reply.started":"2023-02-13T04:21:26.865178Z","shell.execute_reply":"2023-02-13T04:21:34.473995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# video metadata \nvideo_metadata_tr_obj = data_info(video_metadata_tr)\n\nvideo_metadata_tr_obj.info()\nvideo_metadata_tr_obj.uniqueCategory()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:34.476422Z","iopub.execute_input":"2023-02-13T04:21:34.477129Z","iopub.status.idle":"2023-02-13T04:21:34.681205Z","shell.execute_reply.started":"2023-02-13T04:21:34.477087Z","shell.execute_reply":"2023-02-13T04:21:34.679467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# helmet detection from previos year competition\nbaseline_helmets_tr_obj = data_info(baseline_helmets_tr)\n\nbaseline_helmets_tr_obj.info()\nbaseline_helmets_tr_obj.uniqueCategory()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:34.684737Z","iopub.execute_input":"2023-02-13T04:21:34.685432Z","iopub.status.idle":"2023-02-13T04:21:39.024419Z","shell.execute_reply.started":"2023-02-13T04:21:34.685388Z","shell.execute_reply":"2023-02-13T04:21:39.023238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Total number of gameplays and players \nprint(\"Number of gameplays: \",len(tracking_tr['game_play'].unique()))\nprint(\"Number of players: \", len(tracking_tr['nfl_player_id'].unique()))","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:39.025870Z","iopub.execute_input":"2023-02-13T04:21:39.026598Z","iopub.status.idle":"2023-02-13T04:21:39.133355Z","shell.execute_reply.started":"2023-02-13T04:21:39.026556Z","shell.execute_reply":"2023-02-13T04:21:39.132302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Step: A number representing each timestep for each play, starting at 0 at the moment of the play starting, and incrementing by 1 every 0.1 seconds.\ntracking_tr['step'].value_counts().hist()","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:39.134668Z","iopub.execute_input":"2023-02-13T04:21:39.135038Z","iopub.status.idle":"2023-02-13T04:21:39.359444Z","shell.execute_reply.started":"2023-02-13T04:21:39.135001Z","shell.execute_reply":"2023-02-13T04:21:39.358435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Correlation plot for Tracking Data\nplt.figure(figsize=(30,10))\nsns.heatmap(tracking_tr.corr(),annot=True, cmap=\"Reds\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:39.361122Z","iopub.execute_input":"2023-02-13T04:21:39.361491Z","iopub.status.idle":"2023-02-13T04:21:40.992204Z","shell.execute_reply.started":"2023-02-13T04:21:39.361456Z","shell.execute_reply":"2023-02-13T04:21:40.991258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Target Variable Distribution","metadata":{}},{"cell_type":"code","source":"labels_tr['contact'].value_counts().plot.pie(autopct    = '%1.1f%%', title = 'Contact Label Distribution')","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:40.993206Z","iopub.execute_input":"2023-02-13T04:21:40.993561Z","iopub.status.idle":"2023-02-13T04:21:41.151701Z","shell.execute_reply.started":"2023-02-13T04:21:40.993527Z","shell.execute_reply":"2023-02-13T04:21:41.148976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### The data is highly unbalanced ","metadata":{}},{"cell_type":"code","source":"# Step: A number representing each timestep for each play, starting at 0 at the moment of the play starting, and incrementing by 1 every 0.1 seconds.\nlabels_tr['step'].value_counts().hist()","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:41.154827Z","iopub.execute_input":"2023-02-13T04:21:41.156228Z","iopub.status.idle":"2023-02-13T04:21:41.508742Z","shell.execute_reply.started":"2023-02-13T04:21:41.156188Z","shell.execute_reply":"2023-02-13T04:21:41.507703Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.plot(labels_tr['step'],labels_tr['contact'])\nsns.histplot(binwidth=0.5, x=\"step\", hue=\"contact\", data=labels_tr[['step','contact']], stat=\"count\", multiple=\"stack\")","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:41.516196Z","iopub.execute_input":"2023-02-13T04:21:41.518590Z","iopub.status.idle":"2023-02-13T04:21:44.771445Z","shell.execute_reply.started":"2023-02-13T04:21:41.518551Z","shell.execute_reply":"2023-02-13T04:21:44.770434Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_tr.boxplot(by ='contact', column =['step'], grid = False)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:44.772752Z","iopub.execute_input":"2023-02-13T04:21:44.773191Z","iopub.status.idle":"2023-02-13T04:21:48.608528Z","shell.execute_reply.started":"2023-02-13T04:21:44.773154Z","shell.execute_reply":"2023-02-13T04:21:48.607466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Single Gameplay Visualisation","metadata":{}},{"cell_type":"code","source":"#############################################\n# DISPLAYING SYNCED VIDEOS                  #\n# FOR A SINGLE GAMEPLAY                     #\n#############################################\n\nclass gameplay:\n    \n    def __init__(self,gameplay_id,view):\n        self.gameplay_id = gameplay_id\n        self.view = view\n    \n    def play_video(self,video_path):\n        frac = 0.65 # scaling factor for display \n#         print(video_path)\n        display(\n            Video(data=video_path, embed=True, height=int(720*frac), width=int(1280*frac))\n        )\n\n\n    def annotate_video_with_helmets(self, baseline_boxes:pd.DataFrame, verbose=True) -> str:\n        \"\"\"\n        Annotates a video with baseline model boxes and labels.\n        \"\"\"\n        \n        video_path = '/kaggle/input/nfl-player-contact-detection/train/' + str(self.gameplay_id) + '_' + str(self.view) + '.mp4'\n#         print(video_path)\n        \n        video_name = str(self.gameplay_id) + '_' + str(self.view) + '.mp4'\n        play_name = self.gameplay_id\n        HELMET_COLOR = (0, 0, 0) # black\n        baseline_boxes = baseline_boxes.query(\"video==@video_name\")\n\n        # verbose\n        if verbose==True:\n            print(f\"Running for {video_name}\")\n\n        # VideoCapture Object\n        cap  = cv2.VideoCapture(video_path)\n        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n        fps = cap.get(cv2.CAP_PROP_FPS)\n\n        # VideoWriter Object\n        output_path = \"labeled_\" + video_name\n        tmp_output_path = \"tmp_\" + output_path\n        out = cv2.VideoWriter(tmp_output_path, cv2.VideoWriter_fourcc(*'MP4V'), \n                              fps, (width, height))\n\n        # check  if camera opened successfully\n        if cap.isOpened() == False:\n            print('Error opening file')\n\n        # read until video is complete\n        frame  = 1\n        while(cap.isOpened()):\n            # capture frame by frame\n            ret, img = cap.read()\n\n            if ret==True:\n                # add play_name text\n                cv2.putText(img, play_name,\n                           (int(0.01*width), int(0.05*height)),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                           )\n\n                # add frame counter\n                cv2.putText(img, \"Frame: \"+str(frame),\n                           (int(0.75*width), int(0.05*height)),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                           )\n\n                # adding helmet bounding boxes and player tags\n                bbox_set = baseline_boxes.query(\"frame==@frame\")\n                for i, annot in enumerate(zip(np.array(bbox_set[['player_label','left','width','top','height']]))):\n                    player_label, bbox_left, bbox_width, bbox_top, bbox_height = annot[0]\n                    # add helmet bbox\n                    cv2.rectangle(img, \n                                  (bbox_left,bbox_top), (bbox_left+bbox_width, bbox_top+bbox_height),\n                                  HELMET_COLOR,\n                                  1\n                                 )\n                    # add player label\n                    cv2.putText(img, player_label,\n                           (bbox_left+10, bbox_top+10),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                           )\n\n                frame += 1\n                out.write(img) \n\n            # break the loop   \n            else: \n                break\n\n\n        out.release()\n\n        # Not all browsers support the codec, we will re-load the file at tmp_output_path\n        # and convert to a codec that is more broadly readable using ffmpeg\n        if os.path.exists(output_path):\n            os.remove(output_path)\n        subprocess.run(\n            [\n                \"ffmpeg\",\n                \"-i\",\n                tmp_output_path,\n                \"-crf\",\n                \"18\",\n                \"-preset\",\n                \"veryfast\",\n                \"-hide_banner\",\n                \"-loglevel\",\n                \"error\",\n                \"-vcodec\",\n                \"libx264\",\n                output_path,\n            ]\n        )\n        os.remove(tmp_output_path)\n\n        return output_path\n\n\n\n    def create_football_field(self,fig, ax, line_color='black', field_color='white'):\n        \"\"\"\n        Function that plots the football field for viewing players.\n        \"\"\"\n\n        # set field dimensions\n        plt.xlim(0,120)\n        plt.ylim(0,53.3)\n\n        # adding rectangles to the field\n        for i in range(12):\n            rect = patches.Rectangle((10*i,0), 10, 53.3, linewidth=1, edgecolor=line_color, facecolor=field_color)\n            ax.add_patch(rect)\n\n        # configure axes\n        ax.tick_params(\n            axis='both',\n            which='both',\n            direction='in',\n            pad=-40,\n            length = 5,\n            bottom=True,\n            top=True,\n            labeltop=True,\n            labelbottom=True,\n            left=False,\n            right=False,\n            labelleft=False,\n            labelright=False,\n            color=line_color)\n\n        # set ticks on the side of the field\n        ax.set_xticks([i for i in range(10,111)])\n\n        # setting yard marking\n        label_set = []\n        for i in range(1,10):\n            if i<=5:\n                label_set += [\" \" for j in range(9)] + [str(i*10)]\n            else:\n                label_set += [\" \" for j in range(9)] + [str((10-i)*10)]\n        label_set =  [\" \"] + label_set + [\" \" for j in range(10)]\n        ax.set_xticklabels(label_set, fontsize=20, color=line_color)\n\n\n        return fig, ax\n\n\n    def populate_field(self, step:np.int64, player_tracking_data:pd.DataFrame, home_color:str='violet', away_color:str='coral'):\n        \"\"\"\n        populates the field with player tracking data of a play_name and step.\n        \"\"\"\n        play_name = self.gameplay_id\n        \n        # subset data to current play and step \n        step_info = player_tracking_data.query(\"game_play==@play_name and step==@step \").copy()\n\n        # create new field\n        fig, ax= plt.subplots(figsize=(12, 5.33))\n        fig, ax  = self.create_football_field(fig, ax)\n\n        # set title\n        ax.set_title(f'Tracking data for {play_name} at step {step}')\n\n        # populate field with players\n        for row in step_info.iterrows():\n            if row[1]['team'] == 'home':\n                color = home_color\n            else:\n                color = away_color\n            marker1 = MarkerStyle(r'$\\spadesuit$')\n            marker1._transform.rotate_deg(360-row[1]['orientation'])\n            ax.scatter(row[1]['x_position'], row[1]['y_position'], marker=marker1, s=150, color=color)    \n\n        plt.close()\n        return fig, ax\n\n\n    def animate(self, i:int, player_tracking_data:pd.DataFrame, frames, home_color:str='violet', away_color:str='coral'):\n        \"\"\"\n        Function to animate player tracking data\n        \"\"\"\n        \n        play_name = self.gameplay_id\n        # create fresh field\n        ax.clear()\n        self.create_football_field(fig, ax)\n\n        # find appropriate\n        play_info = player_tracking_data.query(\"game_play==@play_name\").copy()\n        step_list = np.linspace(play_info['step'].min(), play_info['step'].max(), frames)\n        step = int(step_list[i])\n\n        # subset data to step info\n        step_info = play_info.query(\"step==@step\").copy()\n\n        # iterate step info to populate field\n        for row in step_info.iterrows():\n            if row[1]['team'] == 'home':\n                color = home_color\n            else:\n                color = away_color\n            marker1 = MarkerStyle(r'$\\spadesuit$')\n            marker1._transform.rotate_deg(360-row[1]['orientation'])\n            ax.scatter(row[1]['x_position'], row[1]['y_position'], marker=marker1, s=150, color=color)\n\n        # set axis title\n        ax.set_title(f'Tracking data for {play_name} at step {step}')\n\n\n    def join_helmets_contact(self, labels, helmets, meta, view, fps=59.94):\n        \"\"\"\n        Joins helmets and labels for a given play_name, Results can be \n        used for visualizing labels.\n        Returns a dataframe with the joint dataframe, duplicating rows if \n        multiple contacts occur.\n        \"\"\"\n        play_name = self.gameplay_id\n        \n        # labels and helmets for specific play_name\n        labels = labels.query('game_play==@play_name').copy()\n        helmets = helmets.query('game_play==@play_name and view==@view').copy()\n\n        meta['start_time'] = pd.to_datetime(meta['start_time'])\n        # start time of the play\n        start_time = meta.query('game_play==@play_name and view==@view')['start_time'].values[0]\n        print(start_time)\n    #     print(type(start_time))\n\n\n        # converting frame into datetime in helmets data for merge\n        helmets['datetime'] = pd.to_timedelta(helmets['frame'] * (1/fps), unit='s') + start_time\n        helmets['datetime'] = pd.to_datetime(helmets['datetime'], utc=True)\n\n        helmets['datetime_ngs'] = pd.DatetimeIndex(helmets['datetime'] + pd.to_timedelta(50, 'ms')).floor('100ms').values\n        helmets['datetime_ngs'] = pd.to_datetime(helmets['datetime_ngs'], utc=True)\n\n\n        # converting datetime in lables for merge\n        labels['datetime_ngs'] = pd.to_datetime(labels['datetime'], utc=True)\n\n        # merge labels and helmets using 'datetime_ngs'\n        play_data = helmets.merge(labels.query('contact==1')[['datetime_ngs', 'nfl_player_id_1', 'nfl_player_id_2', 'contact_id']],\n                                 left_on = ['datetime_ngs', 'nfl_player_id'],\n                                 right_on = ['datetime_ngs', 'nfl_player_id_1'],\n                                 how = 'left')\n\n        return play_data\n\n\n    def annotate_video_with_contact_labels(self, baseline_boxes:pd.DataFrame, verbose=True) -> str:\n        \"\"\"\n        Annotates a video with baseline model boxes and labels.\n        Helmet boxes are colored based on the contact label.\n        \"\"\"\n        \n        video_path = '/kaggle/input/nfl-player-contact-detection/train/' + str(self.gameplay_id) + '_' + str(self.view) + '.mp4'\n        video_name = video_path.split('/')[-1]\n        play_name = video_name.split('.')[0]\n        play_name = '_'.join(play_name.split('_')[:-1])\n        HELMET_COLOR = (0, 0, 0) # black\n        baseline_boxes = baseline_boxes.query(\"game_play==@play_name\").copy()\n\n        # verbose\n        if verbose==True:\n            print(f\"Running for {video_name}\")\n\n        # VideoCapture Object\n        cap  = cv2.VideoCapture(video_path)\n        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n        fps = cap.get(cv2.CAP_PROP_FPS)\n\n        # VideoWriter Object\n        output_path = \"labeled_\" + video_name\n        tmp_output_path = \"tmp_\" + output_path\n        out = cv2.VideoWriter(tmp_output_path, cv2.VideoWriter_fourcc(*'MP4V'), \n                              fps, (width, height))\n\n        # check  if camera opened successfully\n        if cap.isOpened() == False:\n            print('Error opening file')\n\n        # read until video is complete\n        frame  = 1\n        while(cap.isOpened()):\n            # capture frame by frame\n            ret, img = cap.read()\n\n            if ret==True:\n                # add play_name text\n                cv2.putText(img, play_name,\n                           (int(0.01*width), int(0.05*height)),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                           )\n\n                # add frame counter\n                cv2.putText(img, \"Frame: \"+str(frame),\n                           (int(0.75*width), int(0.05*height)),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                           )\n\n                # adding helmet bounding boxes, player tags and contact labels\n                bbox_set = baseline_boxes.query(\"frame==@frame\")\n                 # ensuring null values (no contact) come first, the player-player contact and then player-ground contact\n                bbox_set = bbox_set.sort_values(by='nfl_player_id_2', na_position='first', ascending=True)\n                for idx, row in bbox_set.iterrows():\n                    if pd.isnull(row.contact_id):\n                        # add black (no contact) helmet bbox\n                        cv2.rectangle(img, \n                                      (int(row.left),int(row.top)), \n                                      (int(row.left+row.width), int(row.top+row.height)),\n                                      HELMET_COLOR, # black\n                                      1\n                                     )\n                    else:\n                        if row.nfl_player_id_2=='G':\n                            # add red (contact with ground)helmet bbox\n                            cv2.rectangle(img, \n                                          (int(row.left),int(row.top)), \n                                          (int(row.left+row.width), int(row.top+row.height)),\n                                          (0, 0, 225), # red\n                                          2\n                                         )\n                        else:\n                            # add green (contact with another player) helmet bbox\n                            cv2.rectangle(img, \n                                          (int(row.left),int(row.top)), \n                                          (int(row.left+row.width), int(row.top+row.height)),\n                                          (0, 255, 0), # green\n                                          2\n                                         )\n\n                            # check if player 2 in view\n                            row_player_2 = bbox_set.query('nfl_player_id_2==@row.nfl_player_id_2')\n                            if len(row_player_2)==0:\n                                pass\n                            else:\n                                # add green (contact with another player) helmet bbox (for player 2)\n                                cv2.rectangle(img, \n                                              (int(row_player_2.left.values[0]),int(row_player_2.top.values[0])), \n                                              (int(row_player_2.left.values[0]+row_player_2.width.values[0]), int(row_player_2.top.values[0]+row_player_2.height.values[0])),\n                                              (0, 255, 0), # green\n                                              2\n                                             )\n                                # add blue connecting line between players in contact\n                                cv2.line(img,\n                                        (int(row.left+(row.width/2)), int(row.top+(row.height/2))),\n                                        (int(row_player_2.left.values[0]+(row_player_2.width.values[0]/2)), int(row_player_2.top.values[0]+(row_player_2.height.values[0]/2))),\n                                        (255, 0, 0), # blue\n                                        2\n                                        )\n\n                    # add player label\n                    cv2.putText(img, row.player_label,\n                            (int(row.left+10), int(row.top+10)),\n                            cv2.FONT_HERSHEY_SIMPLEX,\n                            1,\n                            HELMET_COLOR,\n                            1\n                            )\n\n\n                frame += 1\n                out.write(img) \n\n            # break the loop   \n            else: \n                break\n\n\n        out.release()\n\n        # Not all browsers support the codec, we will re-load the file at tmp_output_path\n        # and convert to a codec that is more broadly readable using ffmpeg\n        if os.path.exists(output_path):\n            os.remove(output_path)\n        subprocess.run(\n            [\n                \"ffmpeg\",\n                \"-i\",\n                tmp_output_path,\n                \"-crf\",\n                \"18\",\n                \"-preset\",\n                \"veryfast\",\n                \"-hide_banner\",\n                \"-loglevel\",\n                \"error\",\n                \"-vcodec\",\n                \"libx264\",\n                output_path,\n            ]\n        )\n        os.remove(tmp_output_path)\n\n        return output_path","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-02-13T04:21:48.610734Z","iopub.execute_input":"2023-02-13T04:21:48.611399Z","iopub.status.idle":"2023-02-13T04:21:48.663349Z","shell.execute_reply.started":"2023-02-13T04:21:48.611358Z","shell.execute_reply":"2023-02-13T04:21:48.662300Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Endzone view for Game play: 58168_003392","metadata":{}},{"cell_type":"code","source":"play_name = '58168_003392'\nview = 'Endzone'\nvideo_path = '/kaggle/input/nfl-player-contact-detection/train/' + play_name+ '_' + view + '.mp4'\ngame_play_obj = gameplay(play_name,view)\n\ngame_play_obj.play_video(video_path)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:48.665063Z","iopub.execute_input":"2023-02-13T04:21:48.665416Z","iopub.status.idle":"2023-02-13T04:21:49.021741Z","shell.execute_reply.started":"2023-02-13T04:21:48.665379Z","shell.execute_reply":"2023-02-13T04:21:49.020252Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Endzone view with **helmets annotated** for Game play: 58168_003392","metadata":{}},{"cell_type":"code","source":"path = game_play_obj.annotate_video_with_helmets(baseline_helmets_tr)\ngame_play_obj.play_video(path)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:21:49.024048Z","iopub.execute_input":"2023-02-13T04:21:49.024901Z","iopub.status.idle":"2023-02-13T04:22:13.703341Z","shell.execute_reply.started":"2023-02-13T04:21:49.024847Z","shell.execute_reply":"2023-02-13T04:22:13.699633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Endzone view from **player tracking data** for Game play: 58168_003392","metadata":{}},{"cell_type":"code","source":"# arguments\ndf = tracking_tr.copy()\n\n# total number of frames at 10hz\nframes = len(df.query(\"game_play==@play_name\")['step'].unique())\n\n# frequency modifier factor [min_val:1, max_val:10] (reduce this for smoother tracking but longer rendering time)\nfreq_mod_fac = 5\n# to reduce rendering time we change the frequency to (10/freq_mod_fac)Hz\nframes = int(frames/freq_mod_fac)\ninterval_ms = int(100*freq_mod_fac)\n\n# initialize figure\nfig, ax = plt.subplots(figsize=(12, 5.33))\nfig, ax = game_play_obj.create_football_field(fig, ax)\n\n\n# animate\nanim = animation.FuncAnimation(fig, game_play_obj.animate, fargs=( df, frames),frames=frames, repeat=False, interval=interval_ms)\n\n# embed html video to notebook\nvideo = anim.to_html5_video()\nhtml = IPython.display.HTML(video)\ndisplay(html)\nplt.close()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:22:13.705987Z","iopub.execute_input":"2023-02-13T04:22:13.706882Z","iopub.status.idle":"2023-02-13T04:24:00.360011Z","shell.execute_reply.started":"2023-02-13T04:22:13.706807Z","shell.execute_reply":"2023-02-13T04:24:00.358906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### ","metadata":{}},{"cell_type":"markdown","source":"### Endzone view with **helmet and contact annotated** for Game play: 58168_003392","metadata":{}},{"cell_type":"code","source":"# video_path = '/kaggle/input/nfl-player-contact-detection/train/58168_003392_Endzone.mp4'\n# play_name = '58168_003392'\n# view = 'Endzone'\n\ngp = game_play_obj.join_helmets_contact(labels_tr, baseline_helmets_tr, video_metadata_tr, view=view)\npath = game_play_obj.annotate_video_with_contact_labels(gp)\ngame_play_obj.play_video(path)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:24:00.363591Z","iopub.execute_input":"2023-02-13T04:24:00.363897Z","iopub.status.idle":"2023-02-13T04:24:28.798399Z","shell.execute_reply.started":"2023-02-13T04:24:00.363868Z","shell.execute_reply":"2023-02-13T04:24:28.795949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Black helmet** boxes indicate that the player is not in contact. A unique number (home/visiting combined with jersey number) is shown next to their helmet.\\n\n#### **Green helmet** boxes indicate that the player is in contact with one or more players.\n#### **Red helmet** boxes indicates that the player is in contact with the ground (and possibly another player).\n#### **Blue** lines show the link between players in contact with each other.","metadata":{}},{"cell_type":"markdown","source":"## MODELING USING ONLY TRACKING DATA","metadata":{}},{"cell_type":"code","source":"# P2P only \ndata = labels_tr[labels_tr['nfl_player_id_2'].str.contains('G') == False].copy()\n\ntarget = 'contact'\ntest_size = 0.15\nlabels_X_train, labels_X_test, labels_y_train, labels_y_test = train_test_split(data.loc[:, data.columns != target], data[target], test_size=test_size, stratify = data[target], random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:24:28.800945Z","iopub.execute_input":"2023-02-13T04:24:28.802934Z","iopub.status.idle":"2023-02-13T04:24:35.414932Z","shell.execute_reply.started":"2023-02-13T04:24:28.802859Z","shell.execute_reply":"2023-02-13T04:24:35.413873Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# data prep\nclass prepare_data_for_model_input:\n    \n    def __init__(self,tracking_tr, labels_tr):\n        self.tracking_tr = tracking_tr\n        self.labels_tr = labels_tr\n        \n    def one_hot(self,df, col):\n        one_hot = pd.get_dummies(df[col], prefix = col)\n        # Drop column as it is now encoded\n        df = df.drop(col,axis = 1)\n        # Join the encoded df\n        df = df.join(one_hot)\n    \n        return df\n    \n    def join(self, player_attributes, p2p_only = True):\n        \n#         # Player to Player contact only\n#         if p2p_only:\n#             data = self.labels_tr[self.labels_tr['nfl_player_id_2'].str.contains('G') == False].copy()\n#         else:\n#             data = self.labels_tr.copy()\n        \n        data = self.labels_tr.copy()\n        # segregating attributes of players to extract from tracking data\n        player_attributes_1 = []\n        player_attributes_2 = []\n\n        for col in player_attributes:\n            player_attributes_1.append(col + '_1')\n            player_attributes_2.append(col + '_2')\n        \n        # Adding player 1 attributes\n        data = pd.merge(self.tracking_tr, data, left_on=['game_play','datetime','step','nfl_player_id'],right_on=['game_play','datetime','step','nfl_player_id_1'], how='right')\n        data.rename(columns = dict(zip(player_attributes, player_attributes_1)), inplace=True)\n#         print(\"columns after rename: \" ,data.columns)\n\n        # del unnecessary columns\n        del data['game_key'],data['play_id'], data['nfl_player_id'], data['jersey_number']\n\n#         print(data.columns)\n        \n        # adding player 2 attributes \n        data['nfl_player_id_2'] = data['nfl_player_id_2'].astype(int)\n        data = pd.merge(tracking_tr, data, left_on=['game_play','datetime','step','nfl_player_id'],right_on=['game_play','datetime','step','nfl_player_id_2'], how='right')\n\n        del data['game_play'], data['game_key'],data['play_id'], data['nfl_player_id'], data['jersey_number']\n#         print(data.columns)\n        data.rename(columns=dict(zip(player_attributes, player_attributes_2)), inplace=True)\n#         print( \"After renaming: \",data.columns)\n        \n        # del unnecessary columns\n        del data['datetime'], data['contact_id']  \n        \n        # ONE HOT ENCODING\n#         del data['team_1'], data['team_2'], data['position_1'], data['position_2']\n        data = self.one_hot(data,'team_1')\n        data = self.one_hot(data,'team_2')\n\n        data = self.one_hot(data,'position_1')\n        data = self.one_hot(data,'position_2')\n        \n        return data","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-02-13T04:24:35.416598Z","iopub.execute_input":"2023-02-13T04:24:35.417194Z","iopub.status.idle":"2023-02-13T04:24:35.431066Z","shell.execute_reply.started":"2023-02-13T04:24:35.417153Z","shell.execute_reply":"2023-02-13T04:24:35.430023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class model:\n    \n    def __init__(self, model = 'xgb'):\n        self.model = model\n\n        \n#     def split_data(self, data, target = 'contact',test_size=0.15):\n#         X_train, X_test, y_train, y_test = train_test_split(data.loc[:, data.columns != target], data[target], test_size=test_size, stratify = data['contact'], random_state=42)\n        \n        \n    def train(self, X_train, X_test, y_train, y_test, param_grid, n_iter = 40, cv = 3,eval_metric='auc', use_gpu = True, verbose = 2):\n        \n        self.X_train = X_train\n        self.X_test = X_test\n        self.y_train = y_train\n        self.y_test = y_test\n        \n        if self.model == 'xgb':\n            if use_gpu == True:\n                xgb_base = XGBClassifier(tree_method='gpu_hist',eval_metric=eval_metric)\n                xgb_random = RandomizedSearchCV(estimator = xgb_base, param_distributions = param_grid, \n                                   n_iter = n_iter, cv = cv, verbose = verbose, random_state = 42, \n                                   n_jobs = 1)\n            else:\n                xgb_base = XGBClassifier(eval_metric=eval_metric)\n                xgb_random = RandomizedSearchCV(estimator = xgb_base, param_distributions = param_grid, \n                                   n_iter = n_iter, cv = cv, verbose = verbose, random_state = 42, \n                                   n_jobs = -1)\n\n            # Fit the random search model\n            xgb_random.fit(self.X_train, self.y_train)\n            \n            # View the best parameters from the random search\n            best_param  = xgb_random.best_params_\n            \n            self.best_model = xgb_random\n            \n            return xgb_random\n        \n        \n    def score(self):\n\n        y_pred = self.best_model.predict(self.X_test)\n        predictions = [round(value) for value in y_pred]\n\n        self.predictions = predictions\n\n        return predictions\n\n    def evaluate(self):\n\n        y_pred = self.predictions\n        y_test = self.y_test\n\n        print(\"****Classification report****\")\n        print(classification_report(y_test,y_pred))\n\n        print(\"****roc_auc_score**** \")\n        print(roc_auc_score(y_test,y_pred))\n\n        print(\"****Confusion Matrix****\")\n        print(pd.DataFrame(confusion_matrix(y_test,y_pred)))\n\n        disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(y_test,y_pred))\n        disp.plot()\n\n        plt.show()\n\n        print(\"****Matthews correlation coef****\")\n        print(matthews_corrcoef(y_test,y_pred))           ","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-02-13T04:24:35.434165Z","iopub.execute_input":"2023-02-13T04:24:35.435694Z","iopub.status.idle":"2023-02-13T04:24:35.448589Z","shell.execute_reply.started":"2023-02-13T04:24:35.435656Z","shell.execute_reply":"2023-02-13T04:24:35.447724Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Attributes taken from Tracking Data \nplayer_attributes = [\n 'team',\n 'position',\n 'x_position',\n 'y_position',\n 'speed',\n 'distance',\n 'direction',\n 'orientation',\n 'acceleration',\n 'sa']\n\n\n# data prep\ntrain_obj = prepare_data_for_model_input(tracking_tr, labels_X_train)\n\ntrain = train_obj.join(player_attributes)\n\nprint(train.columns)\n\ntest_obj = prepare_data_for_model_input(tracking_tr, labels_X_test)\n\ntest = test_obj.join(player_attributes)\n\nprint(test.columns)\n","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-02-13T04:24:35.450190Z","iopub.execute_input":"2023-02-13T04:24:35.450591Z","iopub.status.idle":"2023-02-13T04:24:59.687636Z","shell.execute_reply.started":"2023-02-13T04:24:35.450554Z","shell.execute_reply":"2023-02-13T04:24:59.686636Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!nvidia-smi","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:24:59.692005Z","iopub.execute_input":"2023-02-13T04:24:59.694275Z","iopub.status.idle":"2023-02-13T04:25:00.874810Z","shell.execute_reply.started":"2023-02-13T04:24:59.694234Z","shell.execute_reply":"2023-02-13T04:25:00.873560Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#setting grid of selected parameters for iteration\nparam_grid = {'gamma': [0,0.1,0.2,0.4,0.8,1.6,3.2,6.4,12.8,25.6,51.2,102.4, 200],\n              'learning_rate': [0.01, 0.03, 0.06, 0.1, 0.15, 0.2, 0.25, 0.300000012, 0.4, 0.5, 0.6, 0.7],\n              'max_depth': [5,6,7,8,9,10,11,12,13,14],\n              'n_estimators': [50,65,80,100,115,130,150],\n              'reg_alpha': [0,0.1,0.2,0.4,0.8,1.6,3.2,6.4,12.8,25.6,51.2,102.4,200],\n              'reg_lambda': [0,0.1,0.2,0.4,0.8,1.6,3.2,6.4,12.8,25.6,51.2,102.4,200]}","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:25:00.877108Z","iopub.execute_input":"2023-02-13T04:25:00.877533Z","iopub.status.idle":"2023-02-13T04:25:00.885445Z","shell.execute_reply.started":"2023-02-13T04:25:00.877477Z","shell.execute_reply":"2023-02-13T04:25:00.884458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### XGBoost – implementation of the gradient boosting ensemble algorithm. Boosting trains models in succession, with each new model being trained to correct the errors made by the previous ones. Models are added sequentially until no further improvements can be made.\n\n![Screenshot 2023-02-06 at 7.33.27 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"}}},{"cell_type":"markdown","source":"#### Why XGBoost?\n    -Parallelization: sequential tree building using parallelized implementation. \n    -Regularization: In-built regularization parameters to prevent overfitting. \n    -Cross-validation: In-built method for cross validation\n    -Handling Missing Values: done automatically (not required here)\n    -Multi-collinearity: Stable in the presence of multicollinearity.","metadata":{}},{"cell_type":"code","source":"model_obj = model(model = 'xgb')\ntrain_samples= len(train) #1000\ntest_samples= len(test) #100\n\nX_train = train[:train_samples]\nX_test = test[:test_samples]\ny_train = labels_y_train[:train_samples]\ny_test = labels_y_test[:test_samples]\n\nxgb = model_obj.train(X_train = train[:train_samples], X_test = test[:test_samples],  y_train = labels_y_train[:train_samples], y_test = labels_y_test[:test_samples], param_grid=param_grid, n_iter = 40, cv=3, use_gpu = True, verbose=False)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T04:25:05.369928Z","iopub.execute_input":"2023-02-13T04:25:05.370750Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_obj.score()\nmodel_obj.evaluate()","metadata":{"execution":{"iopub.status.idle":"2023-02-13T05:01:17.921814Z","shell.execute_reply.started":"2023-02-13T05:01:12.507030Z","shell.execute_reply":"2023-02-13T05:01:17.920590Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xgb.best_estimator_.save_model(\"/kaggle/working/xgb.json\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plotting feature importance \nsorted_idx = xgb.best_estimator_.feature_importances_.argsort()\nsorted_idx = sorted_idx[len(sorted_idx)-10:]\nplt.barh(train.columns[sorted_idx], xgb.best_estimator_.feature_importances_[sorted_idx])\nplt.xlabel(\"Xgboost Feature Importance\")","metadata":{"execution":{"iopub.status.busy":"2023-02-13T05:43:20.933937Z","iopub.execute_input":"2023-02-13T05:43:20.934307Z","iopub.status.idle":"2023-02-13T05:43:21.162549Z","shell.execute_reply.started":"2023-02-13T05:43:20.934277Z","shell.execute_reply":"2023-02-13T05:43:21.161458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"t1 = time()\nsample_shap = int(len(X_test)*0.05)\n# Fits the explainer\n\nexplainer = shap.Explainer(xgb.predict, X_test[:sample_shap])\nt2 = time()\n\nprint(\"time taken: \", t2-t1)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T05:47:12.039478Z","iopub.execute_input":"2023-02-13T05:47:12.040216Z","iopub.status.idle":"2023-02-13T05:47:12.050353Z","shell.execute_reply.started":"2023-02-13T05:47:12.040180Z","shell.execute_reply":"2023-02-13T05:47:12.049266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"t1 = time()\n\n# Calculates the SHAP values - It takes some time\nshap_values = explainer(X_test[:sample_shap])\n\nt2 = time()\nprint(\"time taken: \", t2-t1)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T05:47:45.041681Z","iopub.execute_input":"2023-02-13T05:47:45.042043Z","iopub.status.idle":"2023-02-13T06:02:00.078633Z","shell.execute_reply.started":"2023-02-13T05:47:45.042011Z","shell.execute_reply":"2023-02-13T06:02:00.077416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shap.plots.bar(shap_values)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T06:02:15.248027Z","iopub.execute_input":"2023-02-13T06:02:15.248422Z","iopub.status.idle":"2023-02-13T06:02:15.567435Z","shell.execute_reply.started":"2023-02-13T06:02:15.248388Z","shell.execute_reply":"2023-02-13T06:02:15.566482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shap.plots.beeswarm(shap_values)","metadata":{"execution":{"iopub.status.busy":"2023-02-13T06:02:22.296157Z","iopub.execute_input":"2023-02-13T06:02:22.296564Z","iopub.status.idle":"2023-02-13T06:02:23.464023Z","shell.execute_reply.started":"2023-02-13T06:02:22.296523Z","shell.execute_reply":"2023-02-13T06:02:23.463029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}