123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
import os
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import sys
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras import layers
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import mlflow
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import mlflow.keras
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from urllib.parse import urlparse
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# mlflow.set_tracking_uri("http://127.0.0.1:5000")
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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mlflow.set_experiment('s444417')
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# train params
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numberOfEpochParam = 0
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learning_rate = 0
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try:
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numberOfEpochParam = int(sys.argv[1])
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learning_rate = int(sys.argv[2])
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except:
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# dafault val
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numberOfEpochParam = 3
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learning_rate = 0.1
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def flatten(t):
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return [item for sublist in t for item in sublist]
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def train():
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with mlflow.start_run():
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# mlflow.tensorflow.autolog()
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cwd = os.path.abspath(os.path.dirname(sys.argv[0]))
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pathTrain = cwd + "/../Participants_Data_HPP/Train.csv"
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pathTest = cwd + "/../Participants_Data_HPP/Test.csv"
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features = ["UNDER_CONSTRUCTION", "RERA", "BHK_NO.", "SQUARE_FT", "READY_TO_MOVE", "RESALE", "LONGITUDE", "LATITUDE", "TARGET(PRICE_IN_LACS)"]
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# get dataset
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house_price_train = pd.read_csv(pathTrain)[features]
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# get test dataset
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house_price_test = pd.read_csv(pathTest)[features]
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house_price_features = house_price_train.copy()
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# pop column
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house_price_labels = house_price_features.pop('TARGET(PRICE_IN_LACS)')
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# process data
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normalize = layers.Normalization()
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normalize.adapt(house_price_features)
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# feature_test_sample = house_price_test.sample(10)
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# labels_test_sample = feature_test_sample.pop('TARGET(PRICE_IN_LACS)')
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house_price_test_features = house_price_test.copy()
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# pop column
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house_price_test_expected = house_price_test_features.pop('TARGET(PRICE_IN_LACS)')
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house_price_features = np.array(house_price_features)
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# load model if exists or create new
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modelPath = 'saved_model/MyModel_tf'
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try:
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linear_model = tf.keras.models.load_model(modelPath)
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print("open existing model")
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except Exception as exception:
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print(exception)
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linear_model = tf.keras.Sequential([
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normalize,
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layers.Dense(1)
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])
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linear_model.compile(loss = tf.losses.MeanSquaredError(),
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optimizer = tf.optimizers.Adam(learning_rate=learning_rate))
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print("creating new model")
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# train model
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history = linear_model.fit(
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house_price_features,
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house_price_labels,
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epochs=int(numberOfEpochParam),
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validation_split=0.33,
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verbose=1,)
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# save model
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linear_model.save(modelPath, save_format='tf')
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# save model as artifact
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# finall loss
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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test_results = {}
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test_results['linear_model'] = linear_model.evaluate(
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house_price_test_features, house_price_test_expected, verbose=0)
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# pred = np.array(linear_model.predict(feature_test_sample))
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# flatten_pred = flatten(pred)
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#
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# with open(cwd + "/../result.txt", "w+") as resultFile:
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# resultFile.write("predictions: " + str(flatten_pred) + '\n')
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# resultFile.write("expected: " + str(labels_test_sample.to_numpy()))
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mlflow.log_param('epochs', numberOfEpochParam)
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mlflow.log_param('learning_rate', learning_rate)
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mlflow.log_metric('final_loss', min(hist["val_loss"]))
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signature = mlflow.models.signature.infer_signature(house_price_features, linear_model.predict(house_price_features))
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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sampleInp = [0.0, 0.0, 2.0, 904.129525, 1.000000, 1.000000, 20.098413, 79.107860]
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# expected value is 49.7
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if tracking_url_type_store != "file":
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mlflow.keras.log_model(linear_model, "linear-model", registered_model_name="HousePriceLinear", signature=signature)
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else:
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mlflow.keras.log_model(linear_model, "model", signature=signature, input_example=np.array(sampleInp))
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if __name__ == '__main__':
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train() |