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diff --git a/exercise_14/starter/evaluate/wandb/run-20220803_231200-2m7twcvg/run-2m7twcvg.wandb b/evaluate/wandb/run-20220803_231200-2m7twcvg/run-2m7twcvg.wandb
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diff --git a/exercise_14/.DS_Store b/exercise_14/.DS_Store
deleted file mode 100644
index 352ce0d..0000000
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diff --git a/exercise_14/.Rhistory b/exercise_14/.Rhistory
deleted file mode 100644
index e69de29..0000000
diff --git a/exercise_14/README.md b/exercise_14/README.md
deleted file mode 100644
index eeb63b1..0000000
--- a/exercise_14/README.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# Instructions
-In this exercise we are going to bring everything together in a complete ML pipeline that
-produces a trained Random Forest model.
-
-# Preliminary step: create a repository and commit and push the starter kit
-1. Create a new GitHub repository called ``genre_classification`` in your GitHub account:
-   ![screenshot](create_github_repo_opt.gif "screenshot")
-2. Clone the repository locally:
-   ```bash
-   git clone [repository_url]
-   ```
-3. Go into the repository and copy there the starter kit, 
-   including all subdirectories:
-   ```bash
-   cd genre_classification
-   cp -r [path to the starter]/starter/* .
-   ```
-4. Add the files of the starter kit to be tracked:
-   ```bash
-   git add .
-   ```
-5. Commit and push:
-   ```bash
-   git commit -m "Starter kit" -a
-   git push
-   ```
-
-# Steps to complete the exercise
-
-The starter kit contains all the steps we have previously completed, only slightly modified to 
-work better together. 
-Your task is to chain them together by completing the ``main.py`` file. Further instructions 
-are contained in the file.
-
-When you are done, commit and push to the repository.
-
-After running the pipeline successfully, go to W&B and tag the exported model as ``prod`` as
-we did in Exercise 13.
-
-A few notes and instructions:
-* When chaining together the steps, the output artifact of a step should be the input artifact
-  of the next one (when applicable). Also use the ``artifact_type`` options so that the final
-  visualization of the pipeline highlights the different steps. For example, you can use
-  ``raw_data`` for the artifact containing the downloaded data, ``preprocessed_data`` for the
-  artifact containing the data after the preprocessing, and so on.
-  
-* For testing, set the ``project_name`` to ``exercise_14``. Once you are done
-  developing, do a production run by changing the ``project_name`` to 
-  ``genre_classification_prod``. This way the visualization of the pipeline will not contain 
-  all your trials and errors. Remember to tag the produced model export as ``prod`` (we are going
-  to use it in the next exercise)
-  
-* When developing, you can override the parameter ``main.execute_steps`` to only execute one or
-  more steps of the pipeline, instead of the entire pipeline. This is useful for debugging. 
-  For example, this only executes the ``random_forest`` step:
-  ```bash
-  mlflow run . -P hydra_options="main.execute_steps='random_forest'"
-  ```
-  and this executes ``download`` and ``preprocess``:
-  ```bash
-  mlflow run . -P hydra_options="main.execute_steps='download,preprocess'"
-  ```
diff --git a/exercise_14/create_github_repo_opt.gif b/exercise_14/create_github_repo_opt.gif
deleted file mode 100644
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diff --git a/exercise_14/solution/check_data/conda.yml b/exercise_14/solution/check_data/conda.yml
deleted file mode 100644
index 343a9c7..0000000
--- a/exercise_14/solution/check_data/conda.yml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - pandas=1.2.3
-  - pip=20.3.3
-  - pytest=6.2.2
-  - scipy=1.6.1
-  - pip:
-      - wandb==0.10.21
\ No newline at end of file
diff --git a/exercise_14/solution/conda.yml b/exercise_14/solution/conda.yml
deleted file mode 100644
index 6efdf27..0000000
--- a/exercise_14/solution/conda.yml
+++ /dev/null
@@ -1,12 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - requests=2.24.0
-  - pip=20.3.3
-  - mlflow=1.14.1
-  - hydra-core=1.0.6
-  - pip:
-      - wandb==0.10.21
-      - hydra-joblib-launcher==1.1.2
diff --git a/exercise_14/solution/config.yaml b/exercise_14/solution/config.yaml
deleted file mode 100644
index 95ccf49..0000000
--- a/exercise_14/solution/config.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-main:
-  project_name: exercise_14
-  experiment_name: dev
-  execute_steps:
-    - download
-    - preprocess
-    - check_data
-    - segregate
-    - random_forest
-    - evaluate
-  # This seed will be used to seed the random number generator
-  # to ensure repeatibility of the data splits and other
-  # pseudo-random operations
-  random_seed: 42
-data:
-  file_url: "https://github.com/udacity/nd0821-c2-build-model-workflow-exercises/blob/master/lesson-2-data-exploration-and-preparation/exercises/exercise_4/starter/genres_mod.parquet?raw=true"
-  reference_dataset: "exercise_14/preprocessed_data.csv:latest"
-  # Threshold for Kolomorov-Smirnov test
-  ks_alpha: 0.05
-  test_size: 0.3
-  val_size: 0.3
-  # Stratify according to the target when splitting the data
-  # in train/test or in train/val
-  stratify: genre
-random_forest_pipeline:
-  random_forest:
-    n_estimators: 100
-    criterion: 'gini'
-    max_depth: 13
-    min_samples_split: 2
-    min_samples_leaf: 1
-    min_weight_fraction_leaf: 0.0
-    max_features: 'auto'
-    max_leaf_nodes: null
-    min_impurity_decrease: 0.0
-    min_impurity_split: null
-    bootstrap: true
-    oob_score: false
-    n_jobs: null
-    # This is a different random seed than main.random_seed,
-    # because this is used only within the RandomForest
-    random_state: 42
-    verbose: 0
-    warm_start: false
-    class_weight: "balanced"
-    ccp_alpha: 0.0
-    max_samples: null
-  tfidf:
-    max_features: 10
-  features:
-    numerical:
-      - "danceability"
-      - "energy"
-      - "loudness"
-      - "speechiness"
-      - "acousticness"
-      - "instrumentalness"
-      - "liveness"
-      - "valence"
-      - "tempo"
-      - "duration_ms"
-    categorical:
-      - "time_signature"
-      - "key"
-    nlp:
-      - "text_feature"
-  export_artifact: "model_export"
diff --git a/exercise_14/solution/download/conda.yml b/exercise_14/solution/download/conda.yml
deleted file mode 100644
index 42cd9c8..0000000
--- a/exercise_14/solution/download/conda.yml
+++ /dev/null
@@ -1,9 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - requests=2.24.0
-  - pip=20.3.3
-  - pip:
-      - wandb==0.10.21
\ No newline at end of file
diff --git a/exercise_14/solution/evaluate/conda.yml b/exercise_14/solution/evaluate/conda.yml
deleted file mode 100644
index 7bdbc70..0000000
--- a/exercise_14/solution/evaluate/conda.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - pandas=1.2.3
-  - pip=20.3.3
-  - scikit-learn=0.24.1
-  - matplotlib==3.2.2
-  - plotly==4.4.1
-  - pillow=8.1.2
-  - mlflow=1.14.1
-  - pip:
-      - wandb==0.10.21
diff --git a/exercise_14/solution/main.py b/exercise_14/solution/main.py
deleted file mode 100644
index 18bfaa7..0000000
--- a/exercise_14/solution/main.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import mlflow
-import os
-import hydra
-from omegaconf import DictConfig, OmegaConf
-
-
-# This automatically reads in the configuration
-@hydra.main(config_name='config')
-def go(config: DictConfig):
-
-    # Setup the wandb experiment. All runs will be grouped under this name
-    os.environ["WANDB_PROJECT"] = config["main"]["project_name"]
-    os.environ["WANDB_RUN_GROUP"] = config["main"]["experiment_name"]
-
-    # You can get the path at the root of the MLflow project with this:
-    root_path = hydra.utils.get_original_cwd()
-
-    # Check which steps we need to execute
-    if isinstance(config["main"]["execute_steps"], str):
-        # This was passed on the command line as a comma-separated list of steps
-        steps_to_execute = config["main"]["execute_steps"].split(",")
-    else:
-
-        steps_to_execute = list(config["main"]["execute_steps"])
-
-    # Download step
-    if "download" in steps_to_execute:
-
-        _ = mlflow.run(
-            os.path.join(root_path, "download"),
-            "main",
-            parameters={
-                "file_url": config["data"]["file_url"],
-                "artifact_name": "raw_data.parquet",
-                "artifact_type": "raw_data",
-                "artifact_description": "Data as downloaded"
-            },
-        )
-
-    if "preprocess" in steps_to_execute:
-        _ = mlflow.run(
-            os.path.join(root_path, "preprocess"),
-            "main",
-            parameters={
-                "input_artifact": "raw_data.parquet:latest",
-                "artifact_name": "preprocessed_data.csv",
-                "artifact_type": "preprocessed_data",
-                "artifact_description": "Data with preprocessing applied"
-            },
-        )
-
-    if "check_data" in steps_to_execute:
-        _ = mlflow.run(
-            os.path.join(root_path, "check_data"),
-            "main",
-            parameters={
-                "reference_artifact": config["data"]["reference_dataset"],
-                "sample_artifact": "preprocessed_data.csv:latest",
-                "ks_alpha": config["data"]["ks_alpha"]
-            },
-        )
-
-    if "segregate" in steps_to_execute:
-
-        _ = mlflow.run(
-            os.path.join(root_path, "segregate"),
-            "main",
-            parameters={
-                "input_artifact": "preprocessed_data.csv:latest",
-                "artifact_root": "data",
-                "artifact_type": "segregated_data",
-                "test_size": config["data"]["test_size"],
-                "stratify": config["data"]["stratify"]
-            },
-        )
-
-    if "random_forest" in steps_to_execute:
-        # Serialize decision tree configuration
-        model_config = os.path.abspath("random_forest_config.yml")
-
-        with open(model_config, "w+") as fp:
-            fp.write(OmegaConf.to_yaml(config["random_forest_pipeline"]))
-
-        _ = mlflow.run(
-            os.path.join(root_path, "random_forest"),
-            "main",
-            parameters={
-                "train_data": "data_train.csv:latest",
-                "model_config": model_config,
-                "export_artifact": config["random_forest_pipeline"]["export_artifact"],
-                "random_seed": config["main"]["random_seed"],
-                "val_size": config["data"]["test_size"],
-                "stratify": config["data"]["stratify"]
-            },
-        )
-
-    if "evaluate" in steps_to_execute:
-
-        _ = mlflow.run(
-            os.path.join(root_path, "evaluate"),
-            "main",
-            parameters={
-                "model_export": f"{config['random_forest_pipeline']['export_artifact']}:latest",
-                "test_data": "data_test.csv:latest"
-            },
-        )
-
-
-if __name__ == "__main__":
-    go()
diff --git a/exercise_14/solution/preprocess/conda.yml b/exercise_14/solution/preprocess/conda.yml
deleted file mode 100644
index 038b0c2..0000000
--- a/exercise_14/solution/preprocess/conda.yml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - pandas=1.2.3
-  - pip=20.3.3
-  - pyarrow=2.0
-  - pip:
-      - wandb==0.10.21
\ No newline at end of file
diff --git a/exercise_14/solution/random_forest/conda.yml b/exercise_14/solution/random_forest/conda.yml
deleted file mode 100644
index c9b150a..0000000
--- a/exercise_14/solution/random_forest/conda.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - pandas=1.2.3
-  - pip=20.3.3
-  - scikit-learn=0.24.1
-  - matplotlib=3.2.2
-  - plotly=4.4.1
-  - pillow=8.1.2
-  - mlflow=1.14.1
-  - pip:
-      - wandb==0.10.21
-      - omegaconf==2.0.6
\ No newline at end of file
diff --git a/exercise_14/solution/segregate/conda.yml b/exercise_14/solution/segregate/conda.yml
deleted file mode 100644
index f4ddc20..0000000
--- a/exercise_14/solution/segregate/conda.yml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: download_data
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - pandas=1.2.3
-  - pip=20.3.3
-  - scikit-learn=0.24.1
-  - pip:
-      - wandb==0.10.21
\ No newline at end of file
diff --git a/exercise_14/starter/MLproject b/exercise_14/starter/MLproject
deleted file mode 100644
index 08ed647..0000000
--- a/exercise_14/starter/MLproject
+++ /dev/null
@@ -1,12 +0,0 @@
-name: download_data
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      hydra_options:
-        description: Hydra parameters to override
-        type: str
-        default: ''
-    command: >-
-      python main.py $(echo {hydra_options})
diff --git a/exercise_14/starter/check_data/MLproject b/exercise_14/starter/check_data/MLproject
deleted file mode 100644
index fbb28da..0000000
--- a/exercise_14/starter/check_data/MLproject
+++ /dev/null
@@ -1,21 +0,0 @@
-name: exercise_9
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      reference_artifact:
-        description: Fully-qualitied name for the artifact to be used as reference dataset
-        type: str
-      sample_artifact:
-        description: Fully-qualitied name for the artifact to be used as new data sample
-        type: str
-      ks_alpha:
-        description: Threshold for the (pre-trial) p-value for the KS test
-        type: float
-    # NOTE: the -s flag is necessary, otherwise pytest will capture all the output and it
-    # will not be uploaded to W&B. Hence, the log in W&B will be empty.
-    command: >-
-      pytest -s -vv . --reference_artifact {reference_artifact} \
-                      --sample_artifact {sample_artifact} \
-                      --ks_alpha {ks_alpha}
diff --git a/exercise_14/starter/check_data/conftest.py b/exercise_14/starter/check_data/conftest.py
deleted file mode 100644
index 6b30ca4..0000000
--- a/exercise_14/starter/check_data/conftest.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pytest
-import pandas as pd
-import wandb
-
-
-run = wandb.init(job_type="data_tests")
-
-
-def pytest_addoption(parser):
-    parser.addoption("--reference_artifact", action="store")
-    parser.addoption("--sample_artifact", action="store")
-    parser.addoption("--ks_alpha", action="store")
-
-
-@pytest.fixture(scope="session")
-def data(request):
-
-    reference_artifact = request.config.option.reference_artifact
-
-    if reference_artifact is None:
-        pytest.fail("--reference_artifact missing on command line")
-
-    sample_artifact = request.config.option.sample_artifact
-
-    if sample_artifact is None:
-        pytest.fail("--sample_artifact missing on command line")
-
-    local_path = run.use_artifact(reference_artifact).file()
-    sample1 = pd.read_csv(local_path)
-
-    local_path = run.use_artifact(sample_artifact).file()
-    sample2 = pd.read_csv(local_path)
-
-    return sample1, sample2
-
-
-@pytest.fixture(scope='session')
-def ks_alpha(request):
-    ks_alpha = request.config.option.ks_alpha
-
-    if ks_alpha is None:
-        pytest.fail("--ks_threshold missing on command line")
-
-    return float(ks_alpha)
diff --git a/exercise_14/starter/check_data/test_data.py b/exercise_14/starter/check_data/test_data.py
deleted file mode 100644
index dd3cefe..0000000
--- a/exercise_14/starter/check_data/test_data.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import scipy.stats
-import pandas as pd
-
-
-def test_column_presence_and_type(data):
-
-    # Disregard the reference dataset
-    _, data = data
-
-    required_columns = {
-        "time_signature": pd.api.types.is_integer_dtype,
-        "key": pd.api.types.is_integer_dtype,
-        "danceability": pd.api.types.is_float_dtype,
-        "energy": pd.api.types.is_float_dtype,
-        "loudness": pd.api.types.is_float_dtype,
-        "speechiness": pd.api.types.is_float_dtype,
-        "acousticness": pd.api.types.is_float_dtype,
-        "instrumentalness": pd.api.types.is_float_dtype,
-        "liveness": pd.api.types.is_float_dtype,
-        "valence": pd.api.types.is_float_dtype,
-        "tempo": pd.api.types.is_float_dtype,
-        "duration_ms": pd.api.types.is_integer_dtype,  # This is integer, not float as one might expect
-        "text_feature": pd.api.types.is_string_dtype,
-        "genre": pd.api.types.is_string_dtype
-    }
-
-    # Check column presence
-    assert set(data.columns.values).issuperset(set(required_columns.keys()))
-
-    for col_name, format_verification_funct in required_columns.items():
-
-        assert format_verification_funct(data[col_name]), f"Column {col_name} failed test {format_verification_funct}"
-
-
-def test_class_names(data):
-
-    # Disregard the reference dataset
-    _, data = data
-
-    # Check that only the known classes are present
-    known_classes = [
-        "Dark Trap",
-        "Underground Rap",
-        "Trap Metal",
-        "Emo",
-        "Rap",
-        "RnB",
-        "Pop",
-        "Hiphop",
-        "techhouse",
-        "techno",
-        "trance",
-        "psytrance",
-        "trap",
-        "dnb",
-        "hardstyle",
-    ]
-
-    assert data["genre"].isin(known_classes).all()
-
-
-def test_column_ranges(data):
-
-    # Disregard the reference dataset
-    _, data = data
-
-    ranges = {
-        "time_signature": (1, 5),
-        "key": (0, 11),
-        "danceability": (0, 1),
-        "energy": (0, 1),
-        "loudness": (-35, 5),
-        "speechiness": (0, 1),
-        "acousticness": (0, 1),
-        "instrumentalness": (0, 1),
-        "liveness": (0, 1),
-        "valence": (0, 1),
-        "tempo": (50, 250),
-        "duration_ms": (20000, 1000000),
-    }
-
-    for col_name, (minimum, maximum) in ranges.items():
-
-        assert data[col_name].dropna().between(minimum, maximum).all(), (
-            f"Column {col_name} failed the test. Should be between {minimum} and {maximum}, "
-            f"instead min={data[col_name].min()} and max={data[col_name].max()}"
-        )
-
-
-def test_kolmogorov_smirnov(data, ks_alpha):
-
-    sample1, sample2 = data
-
-    columns = [
-        "danceability",
-        "energy",
-        "loudness",
-        "speechiness",
-        "acousticness",
-        "instrumentalness",
-        "liveness",
-        "valence",
-        "tempo",
-        "duration_ms"
-    ]
-
-    # Bonferroni correction for multiple hypothesis testing
-    # (see my blog post on this topic to see where this comes from:
-    # https://towardsdatascience.com/precision-and-recall-trade-off-and-multiple-hypothesis-testing-family-wise-error-rate-vs-false-71a85057ca2b)
-    alpha_prime = 1 - (1 - ks_alpha)**(1 / len(columns))
-
-    for col in columns:
-
-        ts, p_value = scipy.stats.ks_2samp(sample1[col], sample2[col])
-
-        # NOTE: as always, the p-value should be interpreted as the probability of
-        # obtaining a test statistic (TS) equal or more extreme that the one we got
-        # by chance, when the null hypothesis is true. If this probability is not
-        # large enough, this dataset should be looked at carefully, hence we fail
-        assert p_value > alpha_prime
diff --git a/exercise_14/starter/download/MLproject b/exercise_14/starter/download/MLproject
deleted file mode 100644
index a0aec05..0000000
--- a/exercise_14/starter/download/MLproject
+++ /dev/null
@@ -1,25 +0,0 @@
-name: download_data
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      file_url:
-        description: URL of the file to download
-        type: uri
-      artifact_name:
-        description: Name for the W&B artifact that will be created
-        type: str
-      artifact_type:
-        description: Type of the artifact to create
-        type: str
-        default: raw_data
-      artifact_description:
-        description: Description for the artifact
-        type: str
-
-    command: >-
-      python download_data.py --file_url {file_url} \
-                              --artifact_name {artifact_name} \
-                              --artifact_type {artifact_type} \
-                              --artifact_description {artifact_description}
diff --git a/exercise_14/starter/download/download_data.py b/exercise_14/starter/download/download_data.py
deleted file mode 100644
index 93a1e1c..0000000
--- a/exercise_14/starter/download/download_data.py
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import logging
-import pathlib
-import wandb
-import requests
-import tempfile
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
-logger = logging.getLogger()
-
-
-def go(args):
-
-    # Derive the base name of the file from the URL
-    basename = pathlib.Path(args.file_url).name.split("?")[0].split("#")[0]
-
-    # Download file, streaming so we can download files larger than
-    # the available memory. We use a named temporary file that gets
-    # destroyed at the end of the context, so we don't leave anything
-    # behind and the file gets removed even in case of errors
-    logger.info(f"Downloading {args.file_url} ...")
-    with tempfile.NamedTemporaryFile(mode='wb+') as fp:
-
-        logger.info("Creating run")
-        with wandb.init(job_type="download_data") as run:
-            # Download the file streaming and write to open temp file
-            with requests.get(args.file_url, stream=True) as r:
-                for chunk in r.iter_content(chunk_size=8192):
-                    fp.write(chunk)
-
-            # Make sure the file has been written to disk before uploading
-            # to W&B
-            fp.flush()
-
-            logger.info("Creating artifact")
-            artifact = wandb.Artifact(
-                name=args.artifact_name,
-                type=args.artifact_type,
-                description=args.artifact_description,
-                metadata={'original_url': args.file_url}
-            )
-            artifact.add_file(fp.name, name=basename)
-
-            logger.info("Logging artifact")
-            run.log_artifact(artifact)
-
-            artifact.wait()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Download a file and upload it as an artifact to W&B", fromfile_prefix_chars="@"
-    )
-
-    parser.add_argument(
-        "--file_url", type=str, help="URL to the input file", required=True
-    )
-
-    parser.add_argument(
-        "--artifact_name", type=str, help="Name for the artifact", required=True
-    )
-
-    parser.add_argument(
-        "--artifact_type", type=str, help="Type for the artifact", required=True
-    )
-
-    parser.add_argument(
-        "--artifact_description",
-        type=str,
-        help="Description for the artifact",
-        required=True,
-    )
-
-    args = parser.parse_args()
-
-    go(args)
diff --git a/exercise_14/starter/evaluate/MLproject b/exercise_14/starter/evaluate/MLproject
deleted file mode 100644
index 07f99a8..0000000
--- a/exercise_14/starter/evaluate/MLproject
+++ /dev/null
@@ -1,15 +0,0 @@
-name: download_data
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      model_export:
-        description: Fully-qualified artifact name for the exported model to evaluate
-        type: str
-      test_data:
-        description: Fully-qualified artifact name for the test data
-        type: str
-    command: >-
-      python run.py --model_export {model_export} \
-                    --test_data {test_data}
diff --git a/exercise_14/starter/evaluate/run.py b/exercise_14/starter/evaluate/run.py
deleted file mode 100644
index d6e1780..0000000
--- a/exercise_14/starter/evaluate/run.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import itertools
-import logging
-import pandas as pd
-import wandb
-import mlflow.sklearn
-import matplotlib.pyplot as plt
-from sklearn.metrics import roc_auc_score, plot_confusion_matrix
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
-logger = logging.getLogger()
-
-
-def go(args):
-
-    run = wandb.init(job_type="test")
-
-    logger.info("Downloading and reading test artifact")
-    test_data_path = run.use_artifact(args.test_data).file()
-    df = pd.read_csv(test_data_path, low_memory=False)
-
-    # Extract the target from the features
-    logger.info("Extracting target from dataframe")
-    X_test = df.copy()
-    y_test = X_test.pop("genre")
-
-    logger.info("Downloading and reading the exported model")
-    model_export_path = run.use_artifact(args.model_export).download()
-
-    pipe = mlflow.sklearn.load_model(model_export_path)
-
-    used_columns = list(itertools.chain.from_iterable([x[2] for x in pipe['preprocessor'].transformers]))
-    pred_proba = pipe.predict_proba(X_test[used_columns])
-
-    logger.info("Scoring")
-    score = roc_auc_score(y_test, pred_proba, average="macro", multi_class="ovo")
-
-    run.summary["AUC"] = score
-
-    logger.info("Computing confusion matrix")
-    fig_cm, sub_cm = plt.subplots(figsize=(10, 10))
-    plot_confusion_matrix(
-        pipe,
-        X_test[used_columns],
-        y_test,
-        ax=sub_cm,
-        normalize="true",
-        values_format=".1f",
-        xticks_rotation=90,
-    )
-    fig_cm.tight_layout()
-
-    run.log(
-        {
-            "confusion_matrix": wandb.Image(fig_cm)
-        }
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Test the provided model on the test artifact",
-        fromfile_prefix_chars="@",
-    )
-
-    parser.add_argument(
-        "--model_export",
-        type=str,
-        help="Fully-qualified artifact name for the exported model to evaluate",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--test_data",
-        type=str,
-        help="Fully-qualified artifact name for the test data",
-        required=True,
-    )
-
-    args = parser.parse_args()
-
-    go(args)
diff --git a/exercise_14/starter/preprocess/MLproject b/exercise_14/starter/preprocess/MLproject
deleted file mode 100644
index 521ed87..0000000
--- a/exercise_14/starter/preprocess/MLproject
+++ /dev/null
@@ -1,25 +0,0 @@
-name: download_data
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      input_artifact:
-        description: Fully qualified name for the artifact
-        type: str
-      artifact_name:
-        description: Name for the W&B artifact that will be created
-        type: str
-      artifact_type:
-        description: Type of the artifact to create
-        type: str
-        default: raw_data
-      artifact_description:
-        description: Description for the artifact
-        type: str
-
-    command: >-
-      python run.py --input_artifact {input_artifact} \
-                              --artifact_name {artifact_name} \
-                              --artifact_type {artifact_type} \
-                              --artifact_description {artifact_description}
diff --git a/exercise_14/starter/preprocess/run.py b/exercise_14/starter/preprocess/run.py
deleted file mode 100644
index 54ad734..0000000
--- a/exercise_14/starter/preprocess/run.py
+++ /dev/null
@@ -1,80 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import logging
-import os
-
-import pandas as pd
-import wandb
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
-logger = logging.getLogger()
-
-
-def go(args):
-
-    run = wandb.init(job_type="process_data")
-
-    logger.info("Downloading artifact")
-    artifact = run.use_artifact(args.input_artifact)
-    artifact_path = artifact.file()
-
-    df = pd.read_parquet(artifact_path)
-
-    # Drop the duplicates
-    logger.info("Dropping duplicates")
-    df = df.drop_duplicates().reset_index(drop=True)
-
-    # A minimal feature engineering step: a new feature
-    logger.info("Feature engineering")
-    df['title'].fillna(value='', inplace=True)
-    df['song_name'].fillna(value='', inplace=True)
-    df['text_feature'] = df['title'] + ' ' + df['song_name']
-
-    filename = "processed_data.csv"
-    df.to_csv(filename)
-
-    artifact = wandb.Artifact(
-        name=args.artifact_name,
-        type=args.artifact_type,
-        description=args.artifact_description,
-    )
-    artifact.add_file(filename)
-
-    logger.info("Logging artifact")
-    run.log_artifact(artifact)
-
-    os.remove(filename)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Preprocess a dataset",
-        fromfile_prefix_chars="@",
-    )
-
-    parser.add_argument(
-        "--input_artifact",
-        type=str,
-        help="Fully-qualified name for the input artifact",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--artifact_name", type=str, help="Name for the artifact", required=True
-    )
-
-    parser.add_argument(
-        "--artifact_type", type=str, help="Type for the artifact", required=True
-    )
-
-    parser.add_argument(
-        "--artifact_description",
-        type=str,
-        help="Description for the artifact",
-        required=True,
-    )
-
-    args = parser.parse_args()
-
-    go(args)
diff --git a/exercise_14/starter/random_forest/MLproject b/exercise_14/starter/random_forest/MLproject
deleted file mode 100644
index 09b52a1..0000000
--- a/exercise_14/starter/random_forest/MLproject
+++ /dev/null
@@ -1,35 +0,0 @@
-name: decision_tree
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      train_data:
-        description: Fully-qualified name for the training data artifact
-        type: str
-      model_config:
-        description: JSON blurb containing the configuration for the decision tree
-        type: str
-      export_artifact:
-        description: Name for the artifact to use for the model export. Use "null" if you do not want to export.
-        type: str
-        default: null
-      random_seed:
-        description: Seed for the random number generator.
-        type: str
-        default: 42
-      val_size:
-        description: Size for the validation set as a fraction of the training set
-        type: number
-        default: 0.3
-      stratify:
-        description: "Name of a column to be used for stratified sampling. Default: 'null', i.e., no stratification"
-        type: str
-        default: null
-    command: >-
-      python run.py --train_data {train_data} \
-                    --model_config {model_config} \
-                    --export_artifact {export_artifact} \
-                    --random_seed {random_seed} \
-                    --val_size {val_size} \
-                    --stratify {stratify}
diff --git a/exercise_14/starter/random_forest/run.py b/exercise_14/starter/random_forest/run.py
deleted file mode 100644
index 06cade8..0000000
--- a/exercise_14/starter/random_forest/run.py
+++ /dev/null
@@ -1,263 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import itertools
-import logging
-import os
-
-import yaml
-import tempfile
-import mlflow
-import pandas as pd
-import numpy as np
-from mlflow.models import infer_signature
-from sklearn.compose import ColumnTransformer
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics import roc_auc_score, plot_confusion_matrix
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import OrdinalEncoder, StandardScaler, FunctionTransformer
-import matplotlib.pyplot as plt
-import wandb
-from sklearn.pipeline import Pipeline, make_pipeline
-from sklearn.impute import SimpleImputer
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
-logger = logging.getLogger()
-
-
-def go(args):
-
-    run = wandb.init(job_type="train")
-
-    logger.info("Downloading and reading train artifact")
-    train_data_path = run.use_artifact(args.train_data).file()
-    df = pd.read_csv(train_data_path, low_memory=False)
-
-    # Extract the target from the features
-    logger.info("Extracting target from dataframe")
-    X = df.copy()
-    y = X.pop("genre")
-
-    logger.info("Splitting train/val")
-    X_train, X_val, y_train, y_val = train_test_split(
-        X,
-        y,
-        test_size=args.val_size,
-        stratify=df[args.stratify] if args.stratify != "null" else None,
-        random_state=args.random_seed,
-    )
-
-    logger.info("Setting up pipeline")
-
-    pipe, used_columns = get_training_inference_pipeline(args)
-
-    logger.info("Fitting")
-    pipe.fit(X_train[used_columns], y_train)
-
-    # Evaluate
-    pred = pipe.predict(X_val[used_columns])
-    pred_proba = pipe.predict_proba(X_val[used_columns])
-
-    logger.info("Scoring")
-    score = roc_auc_score(y_val, pred_proba, average="macro", multi_class="ovo")
-
-    run.summary["AUC"] = score
-
-    # Export if required
-    if args.export_artifact != "null":
-
-        export_model(run, pipe, used_columns, X_val, pred, args.export_artifact)
-
-    # Some useful plots
-    fig_feat_imp = plot_feature_importance(pipe)
-
-    fig_cm, sub_cm = plt.subplots(figsize=(10, 10))
-    plot_confusion_matrix(
-        pipe,
-        X_val[used_columns],
-        y_val,
-        ax=sub_cm,
-        normalize="true",
-        values_format=".1f",
-        xticks_rotation=90,
-    )
-    fig_cm.tight_layout()
-
-    run.log(
-        {
-            "feature_importance": wandb.Image(fig_feat_imp),
-            "confusion_matrix": wandb.Image(fig_cm),
-        }
-    )
-
-
-def export_model(run, pipe, used_columns, X_val, val_pred, export_artifact):
-
-    # Infer the signature of the model
-
-    # Get the columns that we are really using from the pipeline
-    signature = infer_signature(X_val[used_columns], val_pred)
-
-    with tempfile.TemporaryDirectory() as temp_dir:
-
-        export_path = os.path.join(temp_dir, "model_export")
-
-        mlflow.sklearn.save_model(
-            pipe,
-            export_path,
-            serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE,
-            signature=signature,
-            input_example=X_val.iloc[:2],
-        )
-
-        artifact = wandb.Artifact(
-            export_artifact,
-            type="model_export",
-            description="Random Forest pipeline export",
-        )
-        artifact.add_dir(export_path)
-
-        run.log_artifact(artifact)
-
-        # Make sure the artifact is uploaded before the temp dir
-        # gets deleted
-        artifact.wait()
-
-
-def plot_feature_importance(pipe):
-
-    # We collect the feature importance for all non-nlp features first
-    feat_names = np.array(
-        pipe["preprocessor"].transformers[0][-1]
-        + pipe["preprocessor"].transformers[1][-1]
-    )
-    feat_imp = pipe["classifier"].feature_importances_[: len(feat_names)]
-    # For the NLP feature we sum across all the TF-IDF dimensions into a global
-    # NLP importance
-    nlp_importance = sum(pipe["classifier"].feature_importances_[len(feat_names) :])
-    feat_imp = np.append(feat_imp, nlp_importance)
-    feat_names = np.append(feat_names, "title + song_name")
-    fig_feat_imp, sub_feat_imp = plt.subplots(figsize=(10, 10))
-    idx = np.argsort(feat_imp)[::-1]
-    sub_feat_imp.bar(range(feat_imp.shape[0]), feat_imp[idx], color="r", align="center")
-    _ = sub_feat_imp.set_xticks(range(feat_imp.shape[0]))
-    _ = sub_feat_imp.set_xticklabels(feat_names[idx], rotation=90)
-    fig_feat_imp.tight_layout()
-    return fig_feat_imp
-
-
-def get_training_inference_pipeline(args):
-
-    # Get the configuration for the pipeline
-    with open(args.model_config) as fp:
-        model_config = yaml.safe_load(fp)
-    # Add it to the W&B configuration so the values for the hyperparams
-    # are tracked
-    wandb.config.update(model_config)
-
-    # We need 3 separate preprocessing "tracks":
-    # - one for categorical features
-    # - one for numerical features
-    # - one for textual ("nlp") features
-    # Categorical preprocessing pipeline
-    categorical_features = sorted(model_config["features"]["categorical"])
-    categorical_transformer = make_pipeline(
-        SimpleImputer(strategy="constant", fill_value=0), OrdinalEncoder()
-    )
-    # Numerical preprocessing pipeline
-    numeric_features = sorted(model_config["features"]["numerical"])
-    numeric_transformer = make_pipeline(
-        SimpleImputer(strategy="median"), StandardScaler()
-    )
-    # Textual ("nlp") preprocessing pipeline
-    nlp_features = sorted(model_config["features"]["nlp"])
-    # This trick is needed because SimpleImputer wants a 2d input, but
-    # TfidfVectorizer wants a 1d input. So we reshape in between the two steps
-    reshape_to_1d = FunctionTransformer(np.reshape, kw_args={"newshape": -1})
-    nlp_transformer = make_pipeline(
-        SimpleImputer(strategy="constant", fill_value=""),
-        reshape_to_1d,
-        TfidfVectorizer(
-            binary=True, max_features=model_config["tfidf"]["max_features"]
-        ),
-    )
-    # Put the 3 tracks together into one pipeline using the ColumnTransformer
-    # This also drops the columns that we are not explicitly transforming
-    preprocessor = ColumnTransformer(
-        transformers=[
-            ("num", numeric_transformer, numeric_features),
-            ("cat", categorical_transformer, categorical_features),
-            ("nlp1", nlp_transformer, nlp_features),
-        ],
-        remainder="drop",  # This drops the columns that we do not transform
-    )
-
-    # Get a list of the columns we used
-    used_columns = list(itertools.chain.from_iterable([x[2] for x in preprocessor.transformers]))
-
-    # Append classifier to preprocessing pipeline.
-    # Now we have a full prediction pipeline.
-    pipe = Pipeline(
-        steps=[
-            ("preprocessor", preprocessor),
-            ("classifier", RandomForestClassifier(**model_config["random_forest"])),
-        ]
-    )
-    return pipe, used_columns
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Train a Random Forest",
-        fromfile_prefix_chars="@",
-    )
-
-    parser.add_argument(
-        "--train_data",
-        type=str,
-        help="Fully-qualified name for the training data artifact",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--model_config",
-        type=str,
-        help="Path to a YAML file containing the configuration for the random forest",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--export_artifact",
-        type=str,
-        help="Name of the artifact for the exported model. Use 'null' for no export.",
-        required=False,
-        default="null",
-    )
-
-    parser.add_argument(
-        "--random_seed",
-        type=int,
-        help="Seed for the random number generator.",
-        required=False,
-        default=42
-    )
-
-    parser.add_argument(
-        "--val_size",
-        type=float,
-        help="Size for the validation set as a fraction of the training set",
-        required=False,
-        default=0.3
-    )
-
-    parser.add_argument(
-        "--stratify",
-        type=str,
-        help="Name of a column to be used for stratified sampling. Default: 'null', i.e., no stratification",
-        required=False,
-        default="null",
-    )
-
-    args = parser.parse_args()
-
-    go(args)
diff --git a/exercise_14/starter/segregate/MLproject b/exercise_14/starter/segregate/MLproject
deleted file mode 100644
index cd86a73..0000000
--- a/exercise_14/starter/segregate/MLproject
+++ /dev/null
@@ -1,35 +0,0 @@
-name: split_data
-conda_env: conda.yml
-
-entry_points:
-  main:
-    parameters:
-      input_artifact:
-        description: Fully qualified name for the artifact
-        type: str
-      artifact_root:
-        description: Name for the W&B artifact that will be created
-        type: str
-      artifact_type:
-        description: Type of the artifact to create
-        type: str
-        default: raw_data
-      test_size:
-        description: Description for the artifact
-        type: float
-      random_state:
-        description: Integer to use to seed the random number generator
-        type: str
-        default: 42
-      stratify:
-        description: If provided, it is considered a column name to be used for stratified splitting
-        type: str
-        default: "null"
-
-    command: >-
-      python run.py --input_artifact {input_artifact} \
-                    --artifact_root {artifact_root} \
-                    --artifact_type {artifact_type} \
-                    --test_size {test_size} \
-                    --random_state {random_state} \
-                    --stratify {stratify}
diff --git a/exercise_14/starter/segregate/run.py b/exercise_14/starter/segregate/run.py
deleted file mode 100644
index f5af1f6..0000000
--- a/exercise_14/starter/segregate/run.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import logging
-import os
-import tempfile
-
-import pandas as pd
-import wandb
-from sklearn.model_selection import train_test_split
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
-logger = logging.getLogger()
-
-
-def go(args):
-
-    run = wandb.init(job_type="split_data")
-
-    logger.info("Downloading and reading artifact")
-    artifact = run.use_artifact(args.input_artifact)
-    artifact_path = artifact.file()
-
-    df = pd.read_csv(artifact_path, low_memory=False)
-
-    # Split first in model_dev/test, then we further divide model_dev in train and validation
-    logger.info("Splitting data into train, val and test")
-    splits = {}
-
-    splits["train"], splits["test"] = train_test_split(
-        df,
-        test_size=args.test_size,
-        random_state=args.random_state,
-        stratify=df[args.stratify] if args.stratify != 'null' else None,
-    )
-
-    # Save the artifacts. We use a temporary directory so we do not leave
-    # any trace behind
-    with tempfile.TemporaryDirectory() as tmp_dir:
-
-        for split, df in splits.items():
-
-            # Make the artifact name from the provided root plus the name of the split
-            artifact_name = f"{args.artifact_root}_{split}.csv"
-
-            # Get the path on disk within the temp directory
-            temp_path = os.path.join(tmp_dir, artifact_name)
-
-            logger.info(f"Uploading the {split} dataset to {artifact_name}")
-
-            # Save then upload to W&B
-            df.to_csv(temp_path)
-
-            artifact = wandb.Artifact(
-                name=artifact_name,
-                type=args.artifact_type,
-                description=f"{split} split of dataset {args.input_artifact}",
-            )
-            artifact.add_file(temp_path)
-
-            logger.info("Logging artifact")
-            run.log_artifact(artifact)
-
-            # This waits for the artifact to be uploaded to W&B. If you
-            # do not add this, the temp directory might be removed before
-            # W&B had a chance to upload the datasets, and the upload
-            # might fail
-            artifact.wait()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Split a dataset into train and test",
-        fromfile_prefix_chars="@",
-    )
-
-    parser.add_argument(
-        "--input_artifact",
-        type=str,
-        help="Fully-qualified name for the input artifact",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--artifact_root",
-        type=str,
-        help="Root for the names of the produced artifacts. The script will produce 2 artifacts: "
-             "{root}_train.csv and {root}_test.csv",
-        required=True,
-    )
-
-    parser.add_argument(
-        "--artifact_type", type=str, help="Type for the produced artifacts", required=True
-    )
-
-    parser.add_argument(
-        "--test_size",
-        help="Fraction of dataset or number of items to include in the test split",
-        type=float,
-        required=True
-    )
-
-    parser.add_argument(
-        "--random_state",
-        help="An integer number to use to init the random number generator. It ensures repeatibility in the"
-             "splitting",
-        type=int,
-        required=False,
-        default=42
-    )
-
-    parser.add_argument(
-        "--stratify",
-        help="If set, it is the name of a column to use for stratified splitting",
-        type=str,
-        required=False,
-        default='null'  # unfortunately mlflow does not support well optional parameters
-    )
-
-    args = parser.parse_args()
-
-    go(args)
diff --git a/exercise_14/starter/main.py b/main.py
similarity index 100%
rename from exercise_14/starter/main.py
rename to main.py
diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/meta.yaml b/mlruns/0/0a728757eeef474e8f562d34e37abb94/meta.yaml
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rename from exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/meta.yaml
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/params/hydra_options b/mlruns/0/0a728757eeef474e8f562d34e37abb94/params/hydra_options
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rename from exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/params/hydra_options
rename to mlruns/0/0a728757eeef474e8f562d34e37abb94/params/hydra_options
diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.gitRepoURL b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.gitRepoURL
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.backend b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.backend
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.entryPoint b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.entryPoint
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.env b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.env
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rename from exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.project.env
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.source.git.repoURL b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.source.git.repoURL
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.source.name b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.source.name
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diff --git a/exercise_14/starter/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.user b/mlruns/0/0a728757eeef474e8f562d34e37abb94/tags/mlflow.user
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/meta.yaml b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/meta.yaml
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.gitRepoURL b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.gitRepoURL
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.project.entryPoint b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.project.entryPoint
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.project.env b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.project.env
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.git.commit b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.git.commit
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.git.repoURL b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.git.repoURL
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diff --git a/exercise_14/starter/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.name b/mlruns/0/0b68bc63c5b8453a9085b1231e00caf0/tags/mlflow.source.name
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diff --git a/exercise_14/starter/segregate/wandb/run-20220803_225428-1jqwaz5d/files/conda-environment.yaml b/segregate/wandb/run-20220803_225428-1jqwaz5d/files/conda-environment.yaml
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diff --git a/exercise_14/starter/segregate/wandb/run-20220803_231108-1xxrpx3o/files/conda-environment.yaml b/segregate/wandb/run-20220803_231108-1xxrpx3o/files/conda-environment.yaml
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