Infvisfrontendmlbackend

.env

@@ -1,3 +1,4 @@
 #!/bin/sh
 # Docker production build environment
-TAG=rpi-0.18
+# TAG=rpi-0.18
+TAG=0.18

.gitignore

@@ -58,4 +58,6 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-.vscode
+.vscode
+
+.xz

README.md

@@ -21,9 +21,8 @@ Browse the repo:
 About the Project
 =================
 
-Aliro is actively developed by the [Institute for Biomedical Informatics](http://upibi.org) at the University of Pennsylvania.
-Contributors include Heather Williams, Weixuan Fu, William La Cava, Josh Cohen,
-Steve Vitale, Sharon Tartarone, Randal Olson, Patryk Orzechowski, and Jason Moore. 
+Aliro is actively developed by the Center for Artificial Intelligence Research (CAIR) in the [Department of Computational Biomedicine](https://www.cedars-sinai.edu/research/departments-institutes/computational-biomedicine.html) at [Cedars-Sinai Medical Center](https://www.cedars-sinai.org/) in Los Angeles.
+Contributors include Hyunjun Choi, Miguel Hernandez, Nick Matsumoto, Jay Moran, Paul Wang, and Jason Moore (PI).
 
 Cite
 ====

ai/ai.py

@@ -233,7 +233,7 @@ def initialize_recommenders(self, rec_class):
             recArgs = self.DEFAULT_REC_ARGS[pred_type]
             recArgs['ml_p'] = ml_p
 
-            recArgs['serialized_rec_directory'] = 'data/recommenders/pennaiweb'
+            recArgs['serialized_rec_directory'] = '/appsrc/data/recommenders/pennaiweb'
             recArgs['load_serialized_rec'] = "if_exists" 
 
             if kb is not None:

ai/recommender/surprise_recommenders.py

@@ -147,12 +147,49 @@ def _reconstruct_training_data(self, results_data, results_mf=None,
         """
         # update trained dataset models and hash table
         super().update(results_data, results_mf, source)
+
+        print('results_data', results_data)
+        print('results_mf', results_mf)
+        print('source', source)
+
         # updates self.results_df and self.trainset
         self._update_training_data(results_data, shuffle=True)
         # check whether the set train data matches the pickled recommender's
         # training data.
         rowHashes = hash_pandas_object(self.results_df).values
+
+
+
+        # test
+        data = [10,20,30,40,50,60]
+        df = pd.DataFrame(data, columns=['Numbers'])
+        test_df=hash_pandas_object(df).values
+
+        print('test_df', test_df)
+
+
+
+        print('self.results_df', self.results_df)
+
+        print('rowHashes', rowHashes)
         newHash = hashlib.sha256(rowHashes).hexdigest()
+
+        # for rowHash in rowHashes:
+        #     print('rowHash: ', rowHash)
+
+        print('newHash', newHash)
+
+        # test 
+        test_newHash = hashlib.sha256(b"Nobody inspects the spammish repetition").hexdigest()
+        print('test_newHash: ', test_newHash)
+
+        # temporary fix for pickled recommender's not having a hash
+        hasattr(self, 'results_df_hash')
+
+        print('self.results_df_hash', self.results_df_hash)
+
+        print('newHash == self.results_df_hash',newHash == self.results_df_hash)
+
         if hasattr(self, 'results_df_hash'):
             if newHash == self.results_df_hash:
                 logger.info('results_df hashes match')

ai/sklearn/config/classifiers.py

@@ -1,5 +1,6 @@
 classifier_config_dict = {
 
+    # Original six classifiers
     'sklearn.tree.DecisionTreeClassifier': {
         'params': {
             'criterion': ["gini", "entropy"],
@@ -75,5 +76,74 @@
             'bootstrap': [True, False],
             'min_weight_fraction_leaf': [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45]
         }
-    }
+    },
+
+
+
+
+
+    # new classifiers
+    # 'sklearn.ensemble.AdaBoostClassifier': {
+    #     'params': {
+    #         'n_estimators': [100, 500],
+    #         'learning_rate': [0.01, 0.1, 1],
+    #         'algorithm': ["SAMME", "SAMME.R"]
+    #     }
+    # },
+
+
+    # 'sklearn.cluster.KMeans': {
+    #     'params': {
+    #         'n_clusters': [2, 3, 4, 5, 6, 7, 8, 9, 10],
+    #         'init': ["k-means++", "random"],
+    #         'n_init': [10, 20, 30],
+    #         'max_iter': [100, 200, 300, 400, 500],
+    #         'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
+    #     }
+    # },
+
+    # 'sklearn.naive_bayes.GaussianNB': {
+    #     'params': {
+    #         'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
+    #     }
+    # },
+
+    # 'sklearn.naive_bayes.MultinomialNB': {
+    #     'params': {
+    #         'alpha': [0.0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100],
+    #         'fit_prior': [True, False]
+    #     }
+    # },
+
+    # 'sklearn.naive_bayes.BernoulliNB': {
+    #     'params': {
+    #         'alpha': [0.0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100],
+    #         'fit_prior': [True, False]
+    #     }
+    # },
+
+    # 'sklearn.neural_network.MLPClassifier': {
+    #     'params': {
+    #         'hidden_layer_sizes': [(100,), (100, 100), (100, 100, 100)],
+    #         'activation': ["identity", "logistic", "tanh", "relu"],
+    #         'solver': ["lbfgs", "sgd", "adam"],
+    #         'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100],
+    #         'learning_rate': ["constant", "invscaling", "adaptive"],
+    #         'learning_rate_init': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100],
+    #         'power_t': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    #         'max_iter': [100, 500, 1000, 2000, 5000, 10000],
+    #         'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
+    #         'momentum': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    #         'nesterovs_momentum': [True, False],
+    #         'early_stopping': [True, False],
+    #         'beta_1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    #         'beta_2': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    #         'epsilon': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
+    #         'validation_fraction': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+    #         'n_iter_no_change': [5, 10, 20, 50, 100]
+    #     }
+    # }
+
+
+
 }

config/common.env

@@ -18,7 +18,7 @@ MACHINE_HOST=machine
 MACHINE_CONFIG=/appsrc/config/machine_config.json
 MACHINE_SHAP_SAMPLES_KERNEL_EXPLAINER=50
 MACHINE_SHAP_SAMPLES_OTHER_EXPLAINER=100
-EXP_TIMEOUT=10
+EXP_TIMEOUT=100
 DT_MAX_DEPTH=6
 
 STARTUP_DATASET_PATH=/appsrc/data/datasets/user

config/machine_config.json

@@ -1,3 +1,6 @@
+
+
+
 {
   "algorithms": ["DecisionTreeClassifier",
                 "GradientBoostingClassifier",
@@ -10,5 +13,14 @@
                 "SVR",
                 "KNeighborsRegressor",
                 "KernelRidge",
-                "RandomForestRegressor"]
+                "RandomForestRegressor",
+                "AdaBoostClassifier"
+                ,"KMeans"
+                ,"GaussianNB"
+                ,"MultinomialNB"
+                ,"BernoulliNB"
+                ,"MLPClassifier"
+              ]
 }
+
+

docker/dbmongo/Dockerfile

@@ -3,6 +3,12 @@ WORKDIR /opt/
 
 ARG docker_filepath=docker/dbmongo/files
 
+# RUN apt-get update
+# RUN apt-get install gpg wget
+# RUN wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
+# RUN apt-get update
+
+
 #add repo for mongodb
 RUN echo "deb http://repo.mongodb.org/apt/ubuntu xenial/mongodb-org/3.2 multiverse" | tee /etc/apt/sources.list.d/mongodb-org-3.2.list
 RUN apt-get update --fix-missing && \

docker/lab/rpi.Dockerfile

@@ -119,6 +119,9 @@ RUN apt-get update --fix-missing && apt-get install -y \
 # See: https://stackoverflow.com/a/58562728/1730417
 # and: https://askubuntu.com/a/1176271/260220
 RUN apt-get update && apt-get install -y software-properties-common
+# RUN -E add-apt-repository 'ppa:deadsnakes/ppa'
+# RUN -E add-apt-repository ppa:deadsnakes/ppa
+# RUN add-apt-repository 'ppa:deadsnakes/ppa'
 RUN add-apt-repository ppa:deadsnakes/ppa
 RUN apt-get update
 RUN apt-get install -y python3.7 python3.7-dev python3.7-distutils

lab/lab.js

@@ -113,10 +113,15 @@ var upload = multer(); // Store files in memory as Buffer objects
 //app.set('superSecret',config.secret);
 app.use(compression()); // Compress all Express requests
 app.use(favicon(path.join(__dirname, "webapp/dist/favicon.ico"))); // Deal with favicon requests
+// app.use(favicon(path.join(__dirname, "webapp/dist/BrainCircuit.ico"))); // Deal with favicon 
 app.use(express.static(path.join(__dirname, "webapp/dist"), {index: false, maxAge: '1d'})); // Static directory
 app.use(morgan("tiny")); // Log requests
-app.use(bodyParser.urlencoded({extended: false}));
-app.use(bodyParser.json());
+// app.use(bodyParser.urlencoded({extended: false}));
+// app.use(bodyParser.json());
+
+app.use(bodyParser.json({limit: "50mb"}));
+app.use(bodyParser.urlencoded({limit: "50mb", extended: true, parameterLimit:50000}));
+
 app.set('appPath', path.join(path.normalize(__dirname), 'webapp/dist'));
 app.use(express.static(app.get('appPath')));