Adding mlflow

MLproject

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+name: 434704
+
+docker_env:
+    image: jarmosz/ium
+entry_points:
+    main:
+        parameters:
+            verbose: {type: int, default: 100}
+            epochs: {type: int, default: 0}
+        command: "python3 mlflow_exp.py {epochs} {verbose}"

mlflow_exp.py

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+import sys
+
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import os.path
+
+import mlflow
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.layers.experimental import preprocessing
+
+
+with mlflow.start_run():
+
+    arguments = sys.argv[1:]
+
+    verbose = int(arguments[0])
+    epochs = int(arguments[1])
+
+    mlflow.log_param("verbose", verbose)
+    mlflow.log_param("epochs", epochs)
+    pd.set_option("display.max_columns", None)
+
+    # Wczytanie danych
+    train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
+
+    # Stworzenie modelu 
+    columns_to_use = ['Year', 'Runtime', 'Netflix']
+    train_X = tf.convert_to_tensor(train_data[columns_to_use])
+    train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
+
+    normalizer = preprocessing.Normalization(input_shape=[3,])
+    normalizer.adapt(train_X)
+
+    model = keras.Sequential([
+        keras.Input(shape=(len(columns_to_use),)),
+        normalizer,
+        layers.Dense(30, activation='relu'),
+        layers.Dense(10, activation='relu'),
+        layers.Dense(25, activation='relu'),
+        layers.Dense(1)
+    ])
+
+    model.compile(loss='mean_absolute_error',
+                    optimizer=tf.keras.optimizers.Adam(0.001),
+                    metrics=[tf.keras.metrics.RootMeanSquaredError()])
+
+    model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
+
+    model.save('linear_regression.h5')
+
+    # Evaluation
+
+    test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
+
+    columns_to_use = ['Year', 'Runtime', 'Netflix']
+    test_X = tf.convert_to_tensor(test_data[columns_to_use])
+    test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
+
+    scores = model.evaluate(x=test_X, 
+                        y=test_Y)
+    
+    mlflow.log_metric("RMSE", scores[1])

mlruns/0/023db6388f714cdbb05c20e41895edab/meta.yaml

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+artifact_uri: file:///Volumes/seagate/ium_434704/mlruns/0/023db6388f714cdbb05c20e41895edab/artifacts
+end_time: null
+entry_point_name: ''
+experiment_id: '0'
+lifecycle_stage: active
+name: ''
+run_id: 023db6388f714cdbb05c20e41895edab
+run_uuid: 023db6388f714cdbb05c20e41895edab
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1621096782764
+status: 1
+tags: []
+user_id: wj

mlruns/0/023db6388f714cdbb05c20e41895edab/params/epochs

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+0

mlruns/0/023db6388f714cdbb05c20e41895edab/params/verbose

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+100

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.gitRepoURL

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+https://git.wmi.amu.edu.pl/s434704/ium_434704.git

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.project.entryPoint

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+main

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.project.env

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+docker

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.source.git.commit

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+340888294d4cc3b500347513e4793df41b1db84d

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.source.git.repoURL

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+https://git.wmi.amu.edu.pl/s434704/ium_434704.git

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.source.name

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+file:///Volumes/seagate/ium_434704

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.source.type

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+PROJECT

mlruns/0/023db6388f714cdbb05c20e41895edab/tags/mlflow.user

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+wj

mlruns/0/meta.yaml

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+artifact_location: file:///Volumes/seagate/ium_434704/mlruns/0
+experiment_id: '0'
+lifecycle_stage: active
+name: Default