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build/lib/dfpl/__main__.py

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+import os
+from argparse import Namespace
+import logging
+import pathlib
+import dataclasses
+from os import path
+
+
+
+from tensorflow import keras
+import wandb
+
+from dfpl.utils import makePathAbsolute, createDirectory
+from dfpl import options
+from dfpl import fingerprint as fp
+from dfpl import autoencoder as ac
+from dfpl import feedforwardNN as fNN
+from dfpl import predictions
+from dfpl import single_label_model as sl
+from dfpl import normalization
+from sklearn.preprocessing import MinMaxScaler
+import pandas as pd
+import pickle
+import jsonpickle
+from pathlib import Path
+import sys
+sys.path.append("/home/shanavas/PycharmProjects/deepFPlearn/dfpl/")
+from normalization import normalize_acc_values, inverse_transform_predictions
+
+wandb.init()
+
+project_directory = pathlib.Path(".").parent.parent.absolute()
+test_train_opts = options.Options(
+    inputFile=f'{project_directory}/input_datasets/toxcast_regression_AR.csv',
+    outputDir=f'{project_directory}/output_data/console_test',
+    ecWeightsFile=f'{project_directory}/output_data/case_regression_01/AR/ae.encoder.hdf5',
+    ecModelDir=f'{project_directory}/output_data/case_regression_01/AR/saved_model',
+    type='inchi',
+    fpType='topological',
+    epochs=100,
+    batchSize=1024,
+    fpSize=2048,
+    encFPSize=256,
+    enableMultiLabel=False,
+    testSize=0.2,
+    kFolds=1,
+    verbose=2,
+    trainAC=False,
+    trainFNN=True,
+    compressFeatures=False,
+    activationFunction="selu",
+    lossFunction='mae',
+    optimizer='Adam',
+    fnnType='REG',  # todo: replace useRegressionModel with fnnType variable
+    wabTarget='AR',
+    wabTracking=True,
+    normalizeACC = False #dilshana
+)
+
+test_pred_opts = options.Options(
+    inputFile=f"{project_directory}/input_datasets/S_dataset.pkl",
+    outputDir=f"{project_directory}/output_data/console_test",
+    outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv",
+    ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model",
+    fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model",
+    type="smiles",
+    fpType="topological"
+)
+
+
+def train(opts: options.Options):
+    """
+    Run the main training procedure
+    :param opts: Options defining the details of the training
+    """
+
+    if opts.wabTracking:
+        wandb.init(project=f"dfpl-reg-training-{opts.wabTarget}", entity="dfpl_regression", config=vars(opts))
+        # opts = wandb.config
+
+    # df = fp.importDataFile(opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize)
+    df = fp.importDataFile(opts.inputFile, import_function=fp.importCSV, fp_size=opts.fpSize)
+
+    # Create output dir if it doesn't exist
+    createDirectory(opts.outputDir)  # why? we just created that directory in the function before??
+    encoder = None
+
+
+
+
+
+    if opts.trainAC:
+        # train an autoencoder on the full feature matrix
+        encoder = ac.train_full_ac(df, opts)
+
+    if opts.compressFeatures:
+
+        if not opts.trainAC:
+            # load trained model for autoencoder
+            encoder = keras.models.load_model(opts.ecModelDir)
+
+        # compress the fingerprints using the autoencoder
+        df = ac.compress_fingerprints(df, encoder)
+
+    if opts.normalizeACC:   #dilshana
+        df, scaler_path = normalize_acc_values(df, column_name='AR', output_dir=opts.outputDir)
+
+    if opts.trainFNN:
+        # train single label models
+        # fNN.train_single_label_models(df=df, opts=opts)
+        sl.train_single_label_models(df=df, opts=opts)
+
+    # train multi-label models
+    if opts.enableMultiLabel:
+        fNN.train_nn_models_multi(df=df, opts=opts)
+
+
+#dilshana
+
+def predict(opts: options.Options) -> None:
+    """
+    Run prediction given specific options
+    :param opts: Options defining the details of the prediction
+    """
+    df = fp.importDataFile(opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize)
+
+
+    # Create output dir if it doesn't exist
+    createDirectory(opts.outputDir)
+
+    if opts.compressFeatures:
+        # load trained model for autoencoder
+        encoder = keras.models.load_model(opts.ecModelDir)
+       # compress the fingerprints using the autoencoder
+        df = ac.compress_fingerprints(df, encoder)
+
+    logging.info(f"Raw predictions: {df.head()}")
+    # predict
+    df2 = predictions.predict_values(df=df, opts=opts)
+    print(df2.head())
+    logging.info(f"Raw predictions: {df2['predicted'].head()}")
+
+
+    if opts.scalerFilePath:
+
+
+        df2['predicted'] = inverse_transform_predictions(df2['predicted'].values, opts.scalerFilePath)
+
+        #normalized_file = os.path.join(opts.outputDir, "normalized_predictions.csv")
+        #logging.info(f"Saving normalized predictions to {normalized_file}")
+        #df2.to_csv(path_or_buf=normalized_file, index=False)
+
+    else:
+        logging.warning("Normalization is enabled but scalerFilePath is not provided in the options. Skipping normalization step.")
+
+
+    names_columns = [c for c in df2.columns if c not in ['fp', 'fpcompressed']]
+
+    df2[names_columns].to_csv(path_or_buf=path.join(opts.outputDir, opts.outputFile))
+    logging.info(f"Prediction successful. Results written to '{path.join(opts.outputDir, opts.outputFile)}'")
+
+
+
+
+
+
+def createLogger(filename: str) -> None:
+    """
+    Set up a logger for the main function that also saves to a log file
+    """
+    # get root logger and set its level
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    # create file handler which logs info messages
+    fh = logging.FileHandler(filename, mode="w")
+    fh.setLevel(logging.INFO)
+    # create console handler
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.INFO)
+    # create formatter and add it to the handlers
+    formatterFile = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    formatterConsole = logging.Formatter('%(levelname)-8s %(message)s')
+    fh.setFormatter(formatterFile)
+    ch.setFormatter(formatterConsole)
+    # add the handlers to the logger
+    logger.addHandler(fh)
+    logger.addHandler(ch)
+
+
+def main():
+    """
+    Main function that runs training/prediction defined by command line arguments
+    """
+    parser = options.createCommandlineParser()
+    prog_args: Namespace = parser.parse_args()
+
+    try:
+        if prog_args.method == "convert":
+            directory = makePathAbsolute(prog_args.f)
+            if path.isdir(directory):
+                createLogger(path.join(directory, "convert.log"))
+                logging.info(f"Convert all data files in {directory}")
+                fp.convert_all(directory)
+            else:
+                raise ValueError("Input directory is not a directory")
+        if prog_args.method == "train":
+            train_opts = options.Options.fromCmdArgs(prog_args)
+            fixed_opts = dataclasses.replace(
+                train_opts,
+                inputFile=makePathAbsolute(train_opts.inputFile),
+                outputDir=makePathAbsolute(train_opts.outputDir)
+            )
+            createDirectory(fixed_opts.outputDir)
+            createLogger(path.join(fixed_opts.outputDir, "train.log"))
+            logging.info(f"The following arguments are received or filled with default values:\n{fixed_opts}")
+            train(fixed_opts)
+            exit(0)
+        elif prog_args.method == "predict":
+            predict_opts = options.Options.fromCmdArgs(prog_args)
+            fixed_opts = dataclasses.replace(
+                predict_opts,
+                inputFile=makePathAbsolute(predict_opts.inputFile),
+                outputDir=makePathAbsolute(predict_opts.outputDir),
+                outputFile=makePathAbsolute(path.join(predict_opts.outputDir, predict_opts.outputFile)),
+                ecModelDir=makePathAbsolute(predict_opts.ecModelDir),
+                fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir),
+                trainAC=False,
+                trainFNN=False
+            )
+            createDirectory(fixed_opts.outputDir)
+            createLogger(path.join(fixed_opts.outputDir, "predict.log"))
+            logging.info(f"The following arguments are received or filled with default values:\n{prog_args}")
+            predict(fixed_opts)
+            exit(0)
+    except AttributeError as e:
+        print(e)
+        parser.print_usage()
+
+
+if __name__ == '__main__':
+    main()