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s444417 2022-05-14 14:50:00 +02:00
parent 56544cdd80
commit 98460d3504
2 changed files with 16 additions and 16 deletions
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4
lab8/MLproject
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@ -8,6 +8,6 @@ docker_env:
entry_points:
main:
parameters:
numberOfEpochParam: {type: float, default: 3}
epochs: {type: float, default: 3}
learning_rate: {type: float, default: 0.1}
command: "python ./lab8/trainScript.py {numberOfEpochParam} {learning_rate}"
command: "python ./lab8/trainScript.py {epochs} {learning_rate}"

28
lab8/trainScript.py
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@ -9,8 +9,8 @@ import mlflow
import mlflow.keras
from urllib.parse import urlparse
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_tracking_uri("http://127.0.0.1:5000")
# mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment('s444417')
# train params
@ -53,8 +53,8 @@ def train():
normalize = layers.Normalization()
normalize.adapt(house_price_features)
feature_test_sample = house_price_test.sample(10)
labels_test_sample = feature_test_sample.pop('TARGET(PRICE_IN_LACS)')
# feature_test_sample = house_price_test.sample(10)
# labels_test_sample = feature_test_sample.pop('TARGET(PRICE_IN_LACS)')
house_price_test_features = house_price_test.copy()
# pop column
@ -97,16 +97,16 @@ def train():
test_results['linear_model'] = linear_model.evaluate(
house_price_test_features, house_price_test_expected, verbose=0)
pred = np.array(linear_model.predict(feature_test_sample))
flatten_pred = flatten(pred)
# pred = np.array(linear_model.predict(feature_test_sample))
# flatten_pred = flatten(pred)
#
## with open(cwd + "/../result.txt", "w+") as resultFile:
# resultFile.write("predictions: " + str(flatten_pred) + '\n')
# resultFile.write("expected: " + str(labels_test_sample.to_numpy()))
with open(cwd + "/../result.txt", "w+") as resultFile:
resultFile.write("predictions: " + str(flatten_pred) + '\n')
resultFile.write("expected: " + str(labels_test_sample.to_numpy()))
mlflow.log_param('epochs number', numberOfEpochParam)
mlflow.log_param('learning rate', learning_rate)
mlflow.log_metric('val loss', min(hist["val_loss"]))
mlflow.log_param('epochs', numberOfEpochParam)
mlflow.log_param('learning_rate', learning_rate)
mlflow.log_metric('final_loss', min(hist["val_loss"]))
signature = mlflow.models.signature.infer_signature(house_price_features, linear_model.predict(house_price_features))
@ -115,7 +115,7 @@ def train():
sampleInp = [0.0, 0.0, 2.0, 904.129525, 1.000000, 1.000000, 20.098413, 79.107860]
# expected value is 49.7
if tracking_url_type_store != "file":
mlflow.keras.log_model(linear_model, "linear-model", registered_model_name="HousePriceLinear", signature=signature, input_example=np.array(sampleInp))
mlflow.keras.log_model(linear_model, "linear-model", registered_model_name="HousePriceLinear", signature=signature)
else:
mlflow.keras.log_model(linear_model, "model", signature=signature, input_example=np.array(sampleInp))