From 27a8104fe12f2d5c6af479c6d4269cbc994849f0 Mon Sep 17 00:00:00 2001 From: Stephen Date: Sun, 23 Feb 2025 21:39:59 -0700 Subject: [PATCH 1/2] Completed project with all necessary changes --- .github/workflows/manual.yml | 67 +++-- .github/workflows/python-app.yml | 27 ++ local_api.py | 32 +- main.py | 120 +++----- ml/model.py | 28 +- model/encoder.pkl | Bin 0 -> 1999 bytes model/model.pkl | Bin 0 -> 73887126 bytes model_card.md | 55 ++++ model_card.md:Zone.Identifier | 0 model_card_template.md | 18 -- screenshots/continuous_integration.png | Bin 0 -> 15145 bytes screenshots/local_api.png | Bin 0 -> 23997 bytes screenshots/unit_test.png | Bin 0 -> 47641 bytes slice_output.txt | 396 +++++++++++++++++++++++++ test_ml.py | 41 ++- train_model.py | 77 ++--- 16 files changed, 663 insertions(+), 198 deletions(-) create mode 100644 .github/workflows/python-app.yml create mode 100644 model/encoder.pkl create mode 100644 model/model.pkl create mode 100644 model_card.md create mode 100644 model_card.md:Zone.Identifier delete mode 100644 model_card_template.md create mode 100644 screenshots/continuous_integration.png create mode 100644 screenshots/local_api.png create mode 100644 screenshots/unit_test.png create mode 100644 slice_output.txt diff --git a/.github/workflows/manual.yml b/.github/workflows/manual.yml index f43ffbd885..ea5adcf45f 100644 --- a/.github/workflows/manual.yml +++ b/.github/workflows/manual.yml @@ -1,31 +1,42 @@ -name: Python CI +import pickle +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import fbeta_score, precision_score, recall_score +from ml.data import process_data -on: [push] +def train_model(X_train, y_train): + model = RandomForestClassifier(random_state=42) + model.fit(X_train, y_train) + return model -jobs: - build: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.10"] +def compute_model_metrics(y, preds): + fbeta = fbeta_score(y, preds, beta=1, zero_division=1) + precision = precision_score(y, preds, zero_division=1) + recall = recall_score(y, preds, zero_division=1) + return precision, recall, fbeta - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest test_ml.py -v +def inference(model, X): + return model.predict(X) + +def save_model(model, path): + with open(path, 'wb') as f: + pickle.dump(model, f) + +def load_model(path): + with open(path, 'rb') as f: + return pickle.load(f) + +def performance_on_categorical_slice( + data, column_name, slice_value, categorical_features, label, encoder, lb, model +): + slice_data = data[data[column_name] == slice_value] + X_slice, y_slice, _, _ = process_data( + slice_data, + categorical_features=categorical_features, + label=label, + training=False, + encoder=encoder, + lb=lb + ) + preds = inference(model, X_slice) + precision, recall, fbeta = compute_model_metrics(y_slice, preds) + return precision, recall, fbeta diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml new file mode 100644 index 0000000000..4fb8eec773 --- /dev/null +++ b/.github/workflows/python-app.yml @@ -0,0 +1,27 @@ +name: Python CI + +on: [push] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest test_ml.py -v diff --git a/local_api.py b/local_api.py index a3bff2f988..da945cdb66 100644 --- a/local_api.py +++ b/local_api.py @@ -1,14 +1,14 @@ import json - import requests -# TODO: send a GET using the URL http://127.0.0.1:8000 -r = None # Your code here +# DONE: send a GET using the URL http://127.0.0.1:8000 +local_URL = "http://127.0.0.1:8000/" +r = requests.get(local_URL) -# TODO: print the status code -# print() -# TODO: print the welcome message -# print() +# DONE: print the status code +print("Get request status code: ", r.status_code) +# DONE?: print the welcome message +print("Welcome Message:", r.json()) @@ -26,13 +26,17 @@ "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, - "native-country": "United-States", + "native-country": "United-States" } -# TODO: send a POST using the data above -r = None # Your code here -# TODO: print the status code -# print() -# TODO: print the result -# print() +# DONE?: send a POST using the data above +#r = requests.post(local_URL+"data?", +# data = data) +r2 = requests.post(f'{local_URL}data', json=data) + + +# DONE: print the status code +print("Post request status code: ", r2.status_code) +# DONE: print the result +print("Inference result: ", r2.json()) \ No newline at end of file diff --git a/main.py b/main.py index 638e2414de..c8dcf72be1 100644 --- a/main.py +++ b/main.py @@ -1,74 +1,46 @@ -import os - -import pandas as pd -from fastapi import FastAPI -from pydantic import BaseModel, Field - -from ml.data import apply_label, process_data -from ml.model import inference, load_model - -# DO NOT MODIFY -class Data(BaseModel): - age: int = Field(..., example=37) - workclass: str = Field(..., example="Private") - fnlgt: int = Field(..., example=178356) - education: str = Field(..., example="HS-grad") - education_num: int = Field(..., example=10, alias="education-num") - marital_status: str = Field( - ..., example="Married-civ-spouse", alias="marital-status" - ) - occupation: str = Field(..., example="Prof-specialty") - relationship: str = Field(..., example="Husband") - race: str = Field(..., example="White") - sex: str = Field(..., example="Male") - capital_gain: int = Field(..., example=0, alias="capital-gain") - capital_loss: int = Field(..., example=0, alias="capital-loss") - hours_per_week: int = Field(..., example=40, alias="hours-per-week") - native_country: str = Field(..., example="United-States", alias="native-country") - -path = None # TODO: enter the path for the saved encoder -encoder = load_model(path) - -path = None # TODO: enter the path for the saved model -model = load_model(path) - -# TODO: create a RESTful API using FastAPI -app = None # your code here - -# TODO: create a GET on the root giving a welcome message -@app.get("/") -async def get_root(): - """ Say hello!""" - # your code here - pass - - -# TODO: create a POST on a different path that does model inference -@app.post("/data/") -async def post_inference(data: Data): - # DO NOT MODIFY: turn the Pydantic model into a dict. - data_dict = data.dict() - # DO NOT MODIFY: clean up the dict to turn it into a Pandas DataFrame. - # The data has names with hyphens and Python does not allow those as variable names. - # Here it uses the functionality of FastAPI/Pydantic/etc to deal with this. - data = {k.replace("_", "-"): [v] for k, v in data_dict.items()} - data = pd.DataFrame.from_dict(data) - - cat_features = [ - "workclass", - "education", - "marital-status", - "occupation", - "relationship", - "race", - "sex", - "native-country", - ] - data_processed, _, _, _ = process_data( - # your code here - # use data as data input - # use training = False - # do not need to pass lb as input - ) - _inference = None # your code here to predict the result using data_processed - return {"result": apply_label(_inference)} +name: download_file +conda_env: conda.yml + +entry_points: + main: + parameters: + + trainval_artifact: + description: Train dataset + type: string + + val_size: + description: Size of the validation split. Fraction of the dataset, or number of items + type: string + + random_seed: + description: Seed for the random number generator. Use this for reproducibility + type: string + default: 42 + + stratify_by: + description: Column to use for stratification (if any) + type: string + default: 'none' + + rf_config: + description: Random forest configuration. A path to a JSON file with the configuration that will + be passed to the scikit-learn constructor for RandomForestRegressor. + type: string + + max_tfidf_features: + description: Maximum number of words to consider for the TFIDF + type: string + + output_artifact: + description: Name for the output artifact + type: string + + command: >- + python run.py --trainval_artifact {trainval_artifact} \ + --val_size {val_size} \ + --random_seed {random_seed} \ + --stratify_by {stratify_by} \ + --rf_config {rf_config} \ + --max_tfidf_features {max_tfidf_features} \ + --output_artifact {output_artifact} \ No newline at end of file diff --git a/ml/model.py b/ml/model.py index f361110f18..32a211d21a 100644 --- a/ml/model.py +++ b/ml/model.py @@ -1,7 +1,8 @@ import pickle from sklearn.metrics import fbeta_score, precision_score, recall_score from ml.data import process_data -# TODO: add necessary import +from sklearn.ensemble import RandomForestClassifier # or your chosen model +import numpy as np # Optional: implement hyperparameter tuning. def train_model(X_train, y_train): @@ -20,7 +21,9 @@ def train_model(X_train, y_train): Trained machine learning model. """ # TODO: implement the function - pass + model = RandomForestClassifier(random_state=42) + model.fit(X_train, y_train) + return model def compute_model_metrics(y, preds): @@ -60,7 +63,7 @@ def inference(model, X): Predictions from the model. """ # TODO: implement the function - pass + return model.predict(X) def save_model(model, path): """ Serializes model to a file. @@ -73,12 +76,14 @@ def save_model(model, path): Path to save pickle file. """ # TODO: implement the function - pass + with open(path, 'wb') as f: + pickle.dump(model, f) def load_model(path): """ Loads pickle file from `path` and returns it.""" # TODO: implement the function - pass + with open(path, 'rb') as f: + return pickle.load(f) def performance_on_categorical_slice( @@ -117,12 +122,15 @@ def performance_on_categorical_slice( fbeta : float """ - # TODO: implement the function + slice_data = data[data[column_name] == slice_value] X_slice, y_slice, _, _ = process_data( - # your code here - # for input data, use data in column given as "column_name", with the slice_value - # use training = False + slice_data, + categorical_features=categorical_features, + label=label, + training=False, + encoder=encoder, + lb=lb ) - preds = None # your code here to get prediction on X_slice using the inference function + preds = inference(model, X_slice) precision, recall, fbeta = compute_model_metrics(y_slice, preds) return precision, recall, fbeta diff --git 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