{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import boto3\n", "import sagemaker\n", "from sagemaker.session import Session\n", "from sagemaker import get_execution_role\n", "from sagemaker.serializers import CSVSerializer\n", "from sagemaker.image_uris import retrieve\n", "from sagemaker.session import TrainingInput\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy.io as sio\n", "import io, os\n", "import sys\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "import matplotlib.gridspec as gridspec\n", "from sklearn.decomposition import PCA\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get data type to train model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "supported_data_type = ('genomic', 'genomic-clinical', 'genomic-clinical-imaging')\n", "#data_type = 'genomic'\n", "#data_type = 'genomic-clinical'\n", "data_type = 'genomic-clinical-imaging'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up S3 buckets and session" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sm_session = sagemaker.Session()\n", "bucket = sm_session.default_bucket()\n", "region = boto3.Session().region_name\n", "role = get_execution_role()\n", "\n", "boto_session = boto3.Session(region_name=region)\n", "sagemaker_client = boto_session.client(service_name='sagemaker', region_name=region)\n", "featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region)\n", "\n", "feature_store_session = Session(\n", " boto_session=boto_session,\n", " sagemaker_client=sagemaker_client,\n", " sagemaker_featurestore_runtime_client=featurestore_runtime\n", ")\n", "\n", "s3_client = boto3.client('s3', region_name=region)\n", "\n", "default_s3_bucket_name = sm_session.default_bucket()\n", "prefix = 'multi-model-health-ml'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get features from SageMaker FeatureStore based on data type" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sagemaker.feature_store.feature_group import FeatureGroup\n", "\n", "genomic_feature_group_name = 'genomic-feature-group'\n", "clinical_feature_group_name = 'clinical-feature-group'\n", "imaging_feature_group_name = 'imaging-feature-group'\n", "\n", "genomic_feature_group = FeatureGroup(name=genomic_feature_group_name, sagemaker_session=feature_store_session)\n", "clinical_feature_group = FeatureGroup(name=clinical_feature_group_name, sagemaker_session=feature_store_session)\n", "imaging_feature_group = FeatureGroup(name=imaging_feature_group_name, sagemaker_session=feature_store_session)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "genomic_query = genomic_feature_group.athena_query()\n", "clinical_query = clinical_feature_group.athena_query()\n", "imaging_query = imaging_feature_group.athena_query()\n", "\n", "genomic_table = genomic_query.table_name\n", "clinical_table = clinical_query.table_name\n", "imaging_table = imaging_query.table_name\n", "\n", "print('Table names')\n", "print(genomic_table)\n", "print(clinical_table)\n", "print(imaging_table)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_features(data_type, output_location): \n", " if (data_type == 'genomic'):\n", " query_string = f'SELECT * FROM \"{genomic_table}\"'\n", " print(query_string)\n", " genomic_query.run(query_string=query_string, output_location=output_location)\n", " genomic_query.wait()\n", " dataset = genomic_query.as_dataframe()\n", " \n", " # Drop features\n", " l_drop = ['case_id', 'pathologicalmstage', 'eventtime', 'write_time', 'api_invocation_time', 'is_deleted'] \n", " dataset = dataset.drop(l_drop, axis = 1) \n", " \n", " elif (data_type == 'genomic-clinical'):\n", " query_string = f'''SELECT * FROM \"{clinical_table}\"\n", " LEFT JOIN \"{genomic_table}\" ON \"{clinical_table}\".case_id = \"{genomic_table}\".case_id'''\n", " print(query_string)\n", "\n", " genomic_query.run(query_string=query_string, output_location=output_location)\n", " genomic_query.wait()\n", " dataset = genomic_query.as_dataframe()\n", " \n", " # Drop features\n", " l_drop = ['case_id', 'case_id.1', 'survival_status', 'pathologicalmstage', \n", " 'eventtime', 'write_time', 'api_invocation_time', 'is_deleted',\n", " 'eventtime.1', 'write_time.1', 'api_invocation_time.1', 'is_deleted.1']\n", " dataset = dataset.drop(l_drop, axis = 1)\n", " \n", " elif (data_type == 'genomic-clinical-imaging'):\n", " query_string = f'''SELECT \"{genomic_table}\".*, \"{clinical_table}\".*, \"{imaging_table}\".* \n", " FROM \"{genomic_table}\"\n", " LEFT OUTER JOIN \"{clinical_table}\" ON \"{clinical_table}\".case_id = \"{genomic_table}\".case_id\n", " LEFT OUTER JOIN \"{imaging_table}\" ON \"{clinical_table}\".case_id = \"{imaging_table}\".subject\n", " ORDER BY \"{clinical_table}\".case_id ASC'''\n", " print(query_string)\n", " \n", " genomic_query.run(query_string=query_string, output_location=output_location)\n", " genomic_query.wait()\n", " dataset = genomic_query.as_dataframe()\n", " \n", " # Drop features\n", " l_drop = ['case_id', 'case_id.1', 'survival_status', 'pathologicalmstage', \n", " 'eventtime', 'write_time', 'api_invocation_time', 'is_deleted',\n", " 'eventtime.1', 'write_time.1', 'api_invocation_time.1', 'is_deleted.1', \n", " 'eventtime.2', 'write_time.2', 'api_invocation_time.2', 'is_deleted.2']\n", " l_drop_img = ['imagename', 'maskname', 'scandate', 'subject']\n", " l_drop_img += [i for i in dataset.columns.tolist() if 'diagnostics' in i]\n", " \n", " dataset = dataset.drop(l_drop + l_drop_img, axis = 1)\n", " \n", " \n", " elif data_type not in supported_data_type:\n", " raise KeyError(f'data_type {data_type} is not supported for this analysis.')\n", " \n", " return dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fs_output_location = f's3://{default_s3_bucket_name}/{prefix}/feature-store-queries'\n", "dataset = get_features(data_type, fs_output_location)\n", "\n", "# Write to csv in S3 without headers and index column.\n", "filename=f'{data_type}-dataset.csv'\n", "dataset_uri_prefix = f's3://{default_s3_bucket_name}/{prefix}/training_input/';\n", "\n", "dataset.to_csv(filename, header=False, index=False)\n", "s3_client.upload_file(filename, default_s3_bucket_name, f'{prefix}/training_input/{filename}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X = dataset.drop(['survivalstatus'], axis =1)\n", "y = dataset['survivalstatus']\n", "\n", "# %% Clearing NaNs and replacing with zeros\n", "X.fillna(value=0., inplace=True)\n", "## use pandas.fillna because the following two lines throw TypeError\n", "## TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely \n", "## coerced to any supported types according to the casting rule ''safe''\n", "# loc_nan = np.where(np.isnan(X)) \n", "# X.iloc[loc_nan] = 0.\n", "\n", "print ('Number of samples in multimodal data: ',dataset.shape[0])\n", "print ('Number of features in multimodal data: ',dataset.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split data for training and testing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scale features" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Add Feature Scaling\n", "sc = StandardScaler()\n", "sc.fit(X_trainval)\n", "X_trainval_scaled = sc.transform(X_trainval)\n", "X_test_scaled = sc.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get feature importance" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get column header for visualization\n", "header = dataset.columns.values\n", "\n", "sns.set(rc={'figure.figsize':(14,10)})\n", "\n", "names = list(header)\n", "names = names[1:]\n", "\n", "# PCA and feature importance\n", "# Indices depend on the above join operation on multiple tables \n", "indices = {'imaging': 108, 'clinical': 21}\n", "\n", "# Set variance threshold for PCA to 99%\n", "pca_threshold = 0.99\n", "pca = PCA(n_components = pca_threshold)\n", "X_trainval_pca = pca.fit_transform(X_trainval_scaled)\n", "features_projected = abs(pca.components_).T\n", "fn = MinMaxScaler().fit_transform(features_projected*pca.explained_variance_ratio_)\n", "\n", "# Select top 20 PCs to plot\n", "n_pc = 20\n", "fn = fn[:,:n_pc]\n", "\n", "fn_genomic = fn[0:indices['clinical'], :]\n", "fn_clinical = fn[indices['clinical']:indices['imaging'], :]\n", "fn_imaging = fn[indices['imaging']:, :]\n", "\n", "fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, gridspec_kw={'wspace':0.025, 'hspace':0.052, 'height_ratios': [indices['clinical'], indices['imaging']-indices['clinical']+1, X.shape[1]-indices['imaging']]})\n", "\n", "sns.heatmap(fn_genomic, ax=ax1, xticklabels=False, yticklabels=False, cmap=\"Blues\", cbar=True)\n", "sns.heatmap(fn_clinical, ax=ax2, xticklabels=False, yticklabels=False, cmap=\"Reds\", cbar=True)\n", "sns.heatmap(fn_imaging, ax=ax3, yticklabels=False, cmap=\"Greens\", cbar=True)\n", "ax3.set_xlabel('Principal Components')\n", "ax3.set_ylabel('Medical Imaging')\n", "ax2.set_ylabel('Clinical')\n", "ax1.set_ylabel('Genomic')\n", "plt.subplots_adjust(wspace=None, hspace=None)\n", "plt.savefig('pca_matrix.png', dpi=450, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Correlation Circle " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", " \n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "# Principal components to plot\n", "pc_n1 = 4\n", "pc_n2 = 5\n", "r = np.linalg.norm(fn[:,pc_n1:pc_n2], axis=1)\n", "\n", "ix_r = np.argsort(-r)\n", "# Select top 30 features\n", "top_r = 30\n", "for i in range(top_r):\n", " plt.plot([0, fn[ix_r[i],0]], [0, fn[ix_r[i],1]], lw=3., label=names[ix_r[i]])\n", "\n", "temp = np.max(r)\n", "ax = fig.add_subplot(1, 1, 1)\n", "circ = plt.Circle((0, 0), radius=temp, edgecolor=None, facecolor='w')\n", "ax.add_patch(circ)\n", "\n", "plt.xlim([-temp, temp])\n", "plt.ylim([-temp, temp])\n", "plt.legend()\n", "plt.show()\n", "plt.savefig('correlationcircle.png', dpi=300, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run PCA for dimensioanality reduction" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of principal components selected: 65\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_test_pca = pca.transform(X_test_scaled)\n", "F = np.cumsum(pca.explained_variance_ratio_)\n", "\n", "plt.figure()\n", "plt.plot(F)\n", "plt.xlabel('Number of PCs')\n", "plt.ylabel('Explanation of Variance Ratio')\n", "print('Number of principal components selected: ', len(pca.explained_variance_ratio_))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split data for training and validation" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training samples: 76\n", "Number of validation samples: 19\n" ] } ], "source": [ "# Split into training and validation data\n", "X_train_pca, X_val_pca, y_train, y_val = train_test_split(X_trainval_pca, y_trainval, test_size=0.2, random_state=0)\n", "\n", "print('Number of training samples: %d'%len(y_train))\n", "print('Number of validation samples: %d'%len(y_val))\n", "\n", "# Create training, validation, and test data by adding label as the first column and removing headers\n", "X_train_pca = pd.DataFrame.from_records(X_train_pca)\n", "train_data = pd.concat([y_train.reset_index(drop=True), X_train_pca.reset_index(drop=True)], axis=1)\n", "\n", "X_val_pca = pd.DataFrame.from_records(X_val_pca)\n", "validation_data = pd.concat([y_val.reset_index(drop=True), X_val_pca.reset_index(drop=True)], axis=1)\n", "\n", "X_test_pca = pd.DataFrame.from_records(X_test_pca)\n", "test_data = pd.concat([y_test.reset_index(drop=True), X_test_pca.reset_index(drop=True)], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "container = retrieve(\"xgboost\", region=region, version='1.2-1')\n", "\n", "xgb = sagemaker.estimator.Estimator(container,\n", " role, \n", " instance_count=1, \n", " instance_type='ml.m4.xlarge',\n", " output_path='s3://{}/{}/training-output'.format(bucket, prefix),\n", " sagemaker_session=sm_session)\n", "\n", "xgb.set_hyperparameters(eta=0.1, objective='reg:logistic', num_round=10) \n", "\n", "# Save data\n", "train_data.to_csv('train.csv', header=False, index=False)\n", "validation_data.to_csv('validation.csv', header=False, index=False)\n", "test_data.to_csv('test.csv', header=False, index=False)\n", "\n", "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train/train.csv')).upload_file('train.csv')\n", "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'validation/validation.csv')).upload_file('validation.csv')\n", "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'test/test.csv')).upload_file('test.csv')\n", "\n", "s3_input_train = TrainingInput(s3_data='s3://{}/{}/train/train.csv'.format(bucket, prefix), \n", " content_type='text/csv')\n", "s3_input_validation = TrainingInput(s3_data='s3://{}/{}/validation/validation.csv'.format(bucket, prefix), \n", " content_type='text/csv')\n", "\n", "# Train model\n", "xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deploy model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xgb_predictor = xgb.deploy(initial_instance_count = 1, instance_type = 'ml.m4.xlarge', serializer = CSVSerializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def predict(data, rows=500):\n", " l_class = []\n", " split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n", " predictions = ''\n", " for array in split_array:\n", " predictions = xgb_predictor.predict(array).decode('utf-8')\n", " \n", " l_prob = predictions.split(',')\n", " \n", " for prob in l_prob:\n", " if (float(prob) >0.5):\n", " l_class.append(1)\n", " else:\n", " l_class.append(0)\n", " \n", " return l_class\n", "\n", "y_predict = predict(test_data.to_numpy()[:,1:])\n", "\n", "acc = accuracy_score(y_test, y_predict)\n", "f1 = f1_score(y_test, y_predict, average='weighted')\n", "prec = precision_score(y_test, y_predict, average='weighted')\n", "rec = recall_score(y_test, y_predict, average='weighted')\n", "\n", "print('Accuracy: ', acc)\n", "print('F1 score: ', f1)\n", "print('Precision: ', prec)\n", "print('Recall: ', rec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uncomment the next line to delete the endpoint and stop incurring cost." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# xgb_predictor.delete_endpoint()" ] } ], "metadata": { "instance_type": "ml.t3.medium", "kernelspec": { "display_name": "Python 3 (Data Science)", "language": "python", "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }