From 39d1b34dd64306783a4470e3107676cfa94b7994 Mon Sep 17 00:00:00 2001 From: "arno.nikpoosh" Date: Wed, 1 Jul 2026 14:47:49 +0330 Subject: [PATCH] push models and code --- .gitignore | 11 + Dockerfile.mlflow-server | 20 + Dockerfile.model-serve | 22 + README.md | 6 + fetch_clean_sessions.sql | 235 ++ main.py | 6 + .../artifacts/.python-version | 1 + .../artifacts/MLmodel | 54 + .../artifacts/artifacts/input_dim.txt | 1 + .../artifacts/artifacts/model.pt | Bin 0 -> 34715 bytes .../artifacts/artifacts/scaler.pkl | Bin 0 -> 1671 bytes .../artifacts/artifacts/threshold.txt | 1 + .../artifacts/conda.yaml | 111 + .../artifacts/pyproject.toml | 12 + .../artifacts/python_env.yaml | 7 + .../artifacts/python_model.pkl | Bin 0 -> 5436 bytes .../artifacts/requirements.txt | 101 + .../artifacts/uv.lock | 2233 +++++++++++++++++ .../artifacts/.python-version | 1 + .../artifacts/MLmodel | 45 + .../artifacts/artifacts/isolation_forest.pkl | Bin 0 -> 861818 bytes .../artifacts/conda.yaml | 111 + 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+ train_and_register_autoencoder.py | 374 +++ train_and_register_forest.py | 374 +++ train_and_register_nusvm.py | 173 ++ uv.lock | 2233 +++++++++++++++++ 125 files changed, 26986 insertions(+) create mode 100644 .gitignore create mode 100644 Dockerfile.mlflow-server create mode 100644 Dockerfile.model-serve create mode 100644 README.md create mode 100644 fetch_clean_sessions.sql create mode 100644 main.py create mode 100644 mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/.python-version create mode 100644 mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/MLmodel create mode 100644 mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/input_dim.txt create mode 100644 mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/model.pt create mode 100644 mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/scaler.pkl create mode 100644 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train_and_register_nusvm.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9562d3e --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv +.env \ No newline at end of file diff --git a/Dockerfile.mlflow-server b/Dockerfile.mlflow-server new file mode 100644 index 0000000..1f2629e --- /dev/null +++ b/Dockerfile.mlflow-server @@ -0,0 +1,20 @@ +# Dockerfile.mlflow-server +# Runs a local MLflow tracking server with the repository's model registry and artifacts. + +FROM python:3.13-slim + +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl \ + && rm -rf /var/lib/apt/lists/* + +RUN pip install --no-cache-dir mlflow==3.14.0 sqlalchemy + +COPY mlflow.db /app/mlflow.db +COPY mlartifacts /app/mlartifacts + +EXPOSE 8080 + +CMD ["mlflow", "server", "--backend-store-uri", "sqlite:///app/mlflow.db", "--default-artifact-root", "file:///app/mlartifacts", "--host", "0.0.0.0", "--port", "8080", "--workers", "1"] diff --git a/Dockerfile.model-serve b/Dockerfile.model-serve new file mode 100644 index 0000000..f0fb891 --- /dev/null +++ b/Dockerfile.model-serve @@ -0,0 +1,22 @@ +# Dockerfile.model-serve +# Builds a container that serves an MLflow registry model by name and version. + +FROM python:3.13-slim + +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl \ + && rm -rf /var/lib/apt/lists/* + +RUN pip install --no-cache-dir mlflow==3.14.0 + +ARG MODEL_NAME=IsolationForest_Anomaly_Detector +ARG MODEL_VERSION=6 +ENV MODEL_NAME=${MODEL_NAME} +ENV MODEL_VERSION=${MODEL_VERSION} + +EXPOSE 5001 + +CMD ["sh", "-c", "mlflow models serve -m \"models:/$MODEL_NAME/$MODEL_VERSION\" --host 0.0.0.0 --port 5001 --env-manager local"] diff --git a/README.md b/README.md new file mode 100644 index 0000000..ca1d493 --- /dev/null +++ b/README.md @@ -0,0 +1,6 @@ +mlflow server --host 127.0.0.1 --port 8000 + +export MLFLOW_TRACKING_URI=http://localhost:8000 + + +mlflow models serve -m "models:/Autoencoder_Anomaly_Detector/2" --host 0.0.0.0 --port 5001 --env-manager local \ No newline at end of file diff --git a/fetch_clean_sessions.sql b/fetch_clean_sessions.sql new file mode 100644 index 0000000..59ac099 --- /dev/null +++ b/fetch_clean_sessions.sql @@ -0,0 +1,235 @@ +WITH base AS ( + SELECT + person_id, + timestamp, + event, + properties, + row_number() OVER ( + PARTITION BY person_id + ORDER BY timestamp, event + ) AS global_order + FROM events + WHERE timestamp >= toDateTime('{start_date}') + AND timestamp < toDateTime('{end_date}') + AND event NOT IN ('flutter_error', 'platform_error') +), + +ordered_events AS ( + SELECT + *, + lagInFrame(timestamp) OVER ( + PARTITION BY person_id + ORDER BY global_order + ) AS prev_ts, + + lagInFrame(event) OVER ( + PARTITION BY person_id + ORDER BY global_order + ) AS prev_event + FROM base +), + +session_flags AS ( + SELECT + *, + CASE + WHEN prev_ts IS NULL THEN 1 + WHEN event = 'Application Opened' THEN 1 + WHEN prev_event IN ('Application Backgrounded', 'Application Closed') THEN 1 + WHEN dateDiff('minute', prev_ts, timestamp) > 10 THEN 1 + ELSE 0 + END AS is_new_session + FROM ordered_events +), + +sessionized AS ( + SELECT + *, + sum(is_new_session) OVER ( + PARTITION BY person_id + ORDER BY global_order + ) AS session_number + FROM session_flags +), + +session_duration_calc AS ( + SELECT + *, + dateDiff( + 'second', + MIN(timestamp) OVER ( + PARTITION BY person_id, session_number + ), + timestamp + ) AS seconds_since_session_start + FROM sessionized +), + +split_sessions AS ( + SELECT + *, + floor(seconds_since_session_start / 1800) AS session_sub_id + FROM session_duration_calc +), + +session_events AS ( + SELECT + *, + row_number() OVER ( + PARTITION BY person_id, session_number, session_sub_id + ORDER BY timestamp, event + ) AS session_index + FROM split_sessions +), + +inter_event_calc AS ( + SELECT + person_id, + session_number, + session_sub_id, + timestamp, + event, + properties, + session_index, + + lagInFrame(timestamp) OVER ( + PARTITION BY person_id, session_number, session_sub_id + ORDER BY session_index + ) AS prev_ts_in_session, + + CASE + WHEN session_index = 1 THEN NULL + ELSE greatest( + 0, + dateDiff( + 'second', + lagInFrame(timestamp) OVER ( + PARTITION BY person_id, session_number, session_sub_id + ORDER BY session_index + ), + timestamp + ) + ) + END AS inter_event_time_seconds + FROM session_events +), + +session_stats AS ( + SELECT + person_id, + session_number, + session_sub_id, + + COUNT(*) AS event_count, + + uniqExact(event) AS unique_event_types, + + uniqExact( + if( + event = '$screen', + replaceRegexpOne( + JSONExtractString(properties, '$screen_name'), + '\\?.*$', + '' + ), + NULL + ) + ) AS screens_visited, + + MIN(timestamp) AS session_start, + MAX(timestamp) AS session_end, + + dateDiff( + 'second', + MIN(timestamp), + MAX(timestamp) + ) AS duration_seconds, + + COUNT(*) * 60.0 / + greatest( + dateDiff( + 'second', + MIN(timestamp), + MAX(timestamp) + ), + 1 + ) AS events_per_minute, + + AVG(inter_event_time_seconds) AS inter_event_time_mean_seconds, + + median(inter_event_time_seconds) AS inter_event_time_median_seconds, + + max(inter_event_time_seconds) AS max_inter_event_gap_seconds, + + sqrt( + varSamp(inter_event_time_seconds) + ) AS inter_event_time_std_seconds, + + toHour(MIN(timestamp)) AS session_start_hour, + + toDayOfWeek(MIN(timestamp)) AS session_day_of_week, + + uniqExact( + toStartOfHour(timestamp) + ) AS distinct_hours_active + + FROM inter_event_calc + GROUP BY + person_id, + session_number, + session_sub_id + + HAVING + COUNT(*) > 2 + AND dateDiff( + 'second', + MIN(timestamp), + MAX(timestamp) + ) >= 4 +) + +SELECT + person_id, + + concat( + toString(person_id), + '_', + toString(session_number), + '_', + toString(session_sub_id) + ) AS session_id, + + session_start, + -- 24-Hour Cyclical Encoding for Session Start + sin(2 * pi() * (toUnixTimestamp(session_start) - toUnixTimestamp(toStartOfDay(session_start))) / 86400) AS session_start_sin, + cos(2 * pi() * (toUnixTimestamp(session_start) - toUnixTimestamp(toStartOfDay(session_start))) / 86400) AS session_start_cos, + + session_end, + -- 24-Hour Cyclical Encoding for Session End + sin(2 * pi() * (toUnixTimestamp(session_end) - toUnixTimestamp(toStartOfDay(session_end))) / 86400) AS session_end_sin, + cos(2 * pi() * (toUnixTimestamp(session_end) - toUnixTimestamp(toStartOfDay(session_end))) / 86400) AS session_end_cos, + + event_count, + unique_event_types, + screens_visited, + + duration_seconds, + events_per_minute, + + inter_event_time_mean_seconds, + inter_event_time_median_seconds, + inter_event_time_std_seconds, + max_inter_event_gap_seconds, + + session_start_hour, + + session_day_of_week, + -- 7-Day Cyclical Encoding for Day of the Week + sin(2 * pi() * session_day_of_week / 7) AS session_day_of_week_sin, + cos(2 * pi() * session_day_of_week / 7) AS session_day_of_week_cos, + + distinct_hours_active + +FROM session_stats +ORDER BY session_start DESC +LIMIT 10000 \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..22fa3d9 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from fraud-models!") + + +if __name__ == "__main__": + main() diff --git a/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/.python-version b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/MLmodel b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/MLmodel new file mode 100644 index 0000000..f6576c7 --- /dev/null +++ b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/MLmodel @@ -0,0 +1,54 @@ +artifact_path: mlflow-artifacts:/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts +flavors: + python_function: + artifacts: + input_dim: + path: artifacts/input_dim.txt + uri: /tmp/tmpe3qq0qoy/input_dim.txt + scaler: + path: artifacts/scaler.pkl + uri: /tmp/tmpe3qq0qoy/scaler.pkl + threshold: + path: artifacts/threshold.txt + uri: /tmp/tmpe3qq0qoy/threshold.txt + weights: + path: artifacts/model.pt + uri: /tmp/tmpe3qq0qoy/model.pt + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-2dc51e8e79f94c18a0d378720d48a0eb +model_size_bytes: 41835 +model_uuid: m-2dc51e8e79f94c18a0d378720d48a0eb +prompts: null +run_id: fcba7e9d98e747a0b8fb1ac5ed037283 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "float", + "name": "anomaly_score", "required": true}, {"type": "float", "name": "threshold", + "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-07-01 08:04:29.835300' diff --git a/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/input_dim.txt b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/input_dim.txt new file mode 100644 index 0000000..25bf17f --- /dev/null +++ b/mlartifacts/1/models/m-2dc51e8e79f94c18a0d378720d48a0eb/artifacts/artifacts/input_dim.txt @@ -0,0 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+model_uuid: m-31c614c988524630a757d966d2a3220b +prompts: null +run_id: 5fe8485c80d24d909446203658755dc9 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}, {"type": "string", "name": "shap_values", "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-29 13:05:34.467379' diff --git a/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/artifacts/isolation_forest.pkl new file 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charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - llvmlite==0.45.1; platform_machine == "x86_64" and sys_platform == "darwin" + - llvmlite==0.47.0; platform_machine != "x86_64" or sys_platform != "darwin" + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numba==0.62.1; platform_machine == "x86_64" and sys_platform == "darwin" + - numba==0.65.1; platform_machine != "x86_64" or sys_platform != "darwin" + - numpy==2.3.5; platform_machine == "x86_64" and sys_platform == "darwin" + - numpy==2.4.6; platform_machine != "x86_64" or sys_platform != "darwin" + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - shap==0.52.0 + - six==1.17.0 + - skops==0.14.0 + - slicer==0.0.8 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - tqdm==4.68.3 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 + - waitress==3.0.2; sys_platform == "win32" + - wcwidth==0.8.1 + - werkzeug==3.1.8 + - yarl==1.24.2 + - zipp==4.1.0 +name: mlflow-env diff --git a/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/pyproject.toml b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/pyproject.toml new file mode 100644 index 0000000..44ca86f --- /dev/null +++ b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "fraud-models" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mlflow>=3.14.0", + "pandas>=2.3.3", + "scikit-learn>=1.9.0", + "shap>=0.52.0", +] diff --git a/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/python_env.yaml b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/python_env.yaml @@ -0,0 +1,7 @@ +python: 3.13.13 +build_dependencies: +- pip +- setuptools +- wheel +dependencies: +- -r requirements.txt diff --git a/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/python_model.pkl b/mlartifacts/1/models/m-31c614c988524630a757d966d2a3220b/artifacts/python_model.pkl new file mode 100644 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b/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/MLmodel b/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/MLmodel new file mode 100644 index 0000000..7443cf3 --- /dev/null +++ b/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/MLmodel @@ -0,0 +1,45 @@ +artifact_path: mlflow-artifacts:/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts +flavors: + python_function: + artifacts: + model: + path: artifacts/isolation_forest.pkl + uri: /tmp/tmppmg7urf4/isolation_forest.pkl + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-6ac644eb55404d39a88c3828aabc2ada +model_size_bytes: 1212421 +model_uuid: m-6ac644eb55404d39a88c3828aabc2ada +prompts: null +run_id: d6640a1202b44ae5ad3192e70cdf384b +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}, {"type": "string", "name": "shap_values", "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-29 13:09:33.978993' diff --git a/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-6ac644eb55404d39a88c3828aabc2ada/artifacts/artifacts/isolation_forest.pkl new file 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+model_uuid: m-971b20e96e4c45b38da2cf065b748c8e +prompts: null +run_id: 31647408dfb9433aa6fbc14ede01764c +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}, {"type": "string", "name": "shap_values", "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-29 13:02:49.773239' diff --git a/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/artifacts/isolation_forest.pkl new file 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charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - llvmlite==0.45.1; platform_machine == "x86_64" and sys_platform == "darwin" + - llvmlite==0.47.0; platform_machine != "x86_64" or sys_platform != "darwin" + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numba==0.62.1; platform_machine == "x86_64" and sys_platform == "darwin" + - numba==0.65.1; platform_machine != "x86_64" or sys_platform != "darwin" + - numpy==2.3.5; platform_machine == "x86_64" and sys_platform == "darwin" + - numpy==2.4.6; platform_machine != "x86_64" or sys_platform != "darwin" + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - shap==0.52.0 + - six==1.17.0 + - skops==0.14.0 + - slicer==0.0.8 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - tqdm==4.68.3 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 + - waitress==3.0.2; sys_platform == "win32" + - wcwidth==0.8.1 + - werkzeug==3.1.8 + - yarl==1.24.2 + - zipp==4.1.0 +name: mlflow-env diff --git a/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/pyproject.toml b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/pyproject.toml new file mode 100644 index 0000000..44ca86f --- /dev/null +++ b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "fraud-models" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mlflow>=3.14.0", + "pandas>=2.3.3", + "scikit-learn>=1.9.0", + "shap>=0.52.0", +] diff --git a/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/python_env.yaml b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/python_env.yaml @@ -0,0 +1,7 @@ +python: 3.13.13 +build_dependencies: +- pip +- setuptools +- wheel +dependencies: +- -r requirements.txt diff --git a/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/python_model.pkl b/mlartifacts/1/models/m-971b20e96e4c45b38da2cf065b748c8e/artifacts/python_model.pkl new file mode 100644 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b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/MLmodel @@ -0,0 +1,43 @@ +artifact_path: mlflow-artifacts:/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts +flavors: + python_function: + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.sklearn + model_path: model.skops + predict_fn: predict + python_version: 3.13.13 + sklearn: + code: null + pickled_model: model.skops + serialization_format: skops + sklearn_version: 1.9.0 + skops_trusted_types: null +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-a5b341fcd80a45e0a6d086fdcb503e6b +model_size_bytes: 2267941 +model_uuid: m-a5b341fcd80a45e0a6d086fdcb503e6b +prompts: null +run_id: 5554784b7c77407ba2095d40d21843ae +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-22 13:32:22.612442' diff --git a/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/conda.yaml b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/conda.yaml new file mode 100644 index 0000000..a6bbe50 --- /dev/null +++ b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/conda.yaml @@ -0,0 +1,103 @@ +channels: +- conda-forge +dependencies: +- python=3.13.13 +- pip +- pip: + - aiohappyeyeballs==2.6.2 + - aiohttp==3.14.1 + - aiosignal==1.4.0 + - alembic==1.18.4 + - annotated-doc==0.0.4 + - annotated-types==0.7.0 + - anyio==4.14.0 + - attrs==26.1.0 + - blinker==1.9.0 + - cachetools==7.1.4 + - certifi==2026.6.17 + - cffi==2.0.0; platform_python_implementation != "PyPy" + - charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numpy==2.4.6 + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - six==1.17.0 + - skops==0.14.0 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 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zg8<^%J_>*5gn16OltKV;ZC3}{4w;AWOKAXvlYFFrDm_LyWc(_egHdG>KwR6-y8SbX zUs4-VFxDeHPoPz4xp7$7ru;ud>O-iajr0L;m+4B9CY8 literal 0 HcmV?d00001 diff --git a/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/python_env.yaml b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/python_env.yaml @@ -0,0 +1,7 @@ +python: 3.13.13 +build_dependencies: +- pip +- setuptools +- wheel +dependencies: +- -r requirements.txt diff --git a/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/requirements.txt b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/requirements.txt new file mode 100644 index 0000000..8b56ecc --- /dev/null +++ b/mlartifacts/1/models/m-a5b341fcd80a45e0a6d086fdcb503e6b/artifacts/requirements.txt @@ -0,0 +1,93 @@ +aiohappyeyeballs==2.6.2 +aiohttp==3.14.1 +aiosignal==1.4.0 +alembic==1.18.4 +annotated-doc==0.0.4 +annotated-types==0.7.0 +anyio==4.14.0 +attrs==26.1.0 +blinker==1.9.0 +cachetools==7.1.4 +certifi==2026.6.17 +cffi==2.0.0; platform_python_implementation != "PyPy" +charset-normalizer==3.4.7 +click==8.4.1 +cloudpickle==3.1.2 +colorama==0.4.6; sys_platform == "win32" +contourpy==1.3.3 +cryptography==48.0.1 +cycler==0.12.1 +databricks-sdk==0.118.0 +docker==7.1.0 +fastapi==0.138.0 +flask-cors==6.0.5 +flask==3.1.3 +fonttools==4.63.0 +frozenlist==1.8.0 +gitdb==4.0.12 +gitpython==3.1.50 +google-auth==2.55.0 +graphene==3.4.3 +graphql-core==3.2.11 +graphql-relay==3.2.0 +greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" +gunicorn==26.0.0; sys_platform != "win32" +h11==0.16.0 +huey==3.0.3 +idna==3.18 +importlib-metadata==9.0.0 +itsdangerous==2.2.0 +jinja2==3.1.6 +joblib==1.5.3 +kiwisolver==1.5.0 +mako==1.3.12 +markupsafe==3.0.3 +matplotlib==3.11.0 +mlflow-skinny==3.14.0 +mlflow-tracing==3.14.0 +mlflow==3.14.0 +multidict==6.7.1 +narwhals==2.22.1 +numpy==2.4.6 +opentelemetry-api==1.42.1 +opentelemetry-proto==1.42.1 +opentelemetry-sdk==1.42.1 +opentelemetry-semantic-conventions==0.63b1 +packaging==26.2 +pandas==2.3.3 +pillow==12.2.0 +prettytable==3.17.0 +propcache==0.5.2 +protobuf==6.33.6 +pyarrow==24.0.0 +pyasn1-modules==0.4.2 +pyasn1==0.6.3 +pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation != "PyPy" +pydantic-core==2.46.4 +pydantic==2.13.4 +pyparsing==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.2 +pytz==2026.2 +pywin32==312; sys_platform == "win32" +pyyaml==6.0.3 +requests==2.34.2 +scikit-learn==1.9.0 +scipy==1.18.0 +six==1.17.0 +skops==0.14.0 +smmap==5.0.3 +sqlalchemy==2.0.51 +sqlparse==0.5.5 +starlette==1.3.1 +threadpoolctl==3.6.0 +typing-extensions==4.15.0 +typing-inspection==0.4.2 +tzdata==2026.2 +urllib3==2.7.0 +uvicorn==0.49.0 +waitress==3.0.2; sys_platform == "win32" +wcwidth==0.8.1 +werkzeug==3.1.8 +yarl==1.24.2 +zipp==4.1.0 \ No newline at end of file diff --git a/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/MLmodel b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/MLmodel new file mode 100644 index 0000000..e9e6d83 --- /dev/null +++ b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/MLmodel @@ -0,0 +1,43 @@ +artifact_path: mlflow-artifacts:/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts +flavors: + python_function: + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.sklearn + model_path: model.skops + predict_fn: predict + python_version: 3.13.13 + sklearn: + code: null + pickled_model: model.skops + serialization_format: skops + sklearn_version: 1.9.0 + skops_trusted_types: null +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-acf59e64090c4983aedf4f38e00e88d8 +model_size_bytes: 2295049 +model_uuid: m-acf59e64090c4983aedf4f38e00e88d8 +prompts: null +run_id: de53e59b4bc94d78b16095f85c342d5a +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-22 09:04:35.192681' diff --git a/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/conda.yaml b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/conda.yaml new file mode 100644 index 0000000..a6bbe50 --- /dev/null +++ b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/conda.yaml @@ -0,0 +1,103 @@ +channels: +- conda-forge +dependencies: +- python=3.13.13 +- pip +- pip: + - aiohappyeyeballs==2.6.2 + - aiohttp==3.14.1 + - aiosignal==1.4.0 + - alembic==1.18.4 + - annotated-doc==0.0.4 + - annotated-types==0.7.0 + - anyio==4.14.0 + - attrs==26.1.0 + - blinker==1.9.0 + - cachetools==7.1.4 + - certifi==2026.6.17 + - cffi==2.0.0; platform_python_implementation != "PyPy" + - charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numpy==2.4.6 + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - six==1.17.0 + - skops==0.14.0 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 + - waitress==3.0.2; sys_platform == "win32" + - wcwidth==0.8.1 + - werkzeug==3.1.8 + - yarl==1.24.2 + - zipp==4.1.0 +name: mlflow-env diff --git a/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/model.skops b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/model.skops new file mode 100644 index 0000000000000000000000000000000000000000..ebabe5b87adab61b48ee0103497129ef2165b0e2 GIT binary patch literal 2295049 zcmeEv30#cb7yl$mghV7;8zq%?A>F4{i54kIDNBTqMA^q4k|HD`ghbjGNmB|*luG+D 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zs5_cDLjgb`6y=8{c~07dTq-^rmY_ru0)*7Mf~lK78c)S%L%^TO2oO^18XjmpTrJ_% z6aYwzK<2L)|9dw5IvbYY)EEQ^`AEY%J-a=RE@nf(C*u(y3{t{&Qy@s literal 0 HcmV?d00001 diff --git a/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/python_env.yaml b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/python_env.yaml @@ -0,0 +1,7 @@ +python: 3.13.13 +build_dependencies: +- pip +- setuptools +- wheel +dependencies: +- -r requirements.txt diff --git a/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/requirements.txt b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/requirements.txt new file mode 100644 index 0000000..8b56ecc --- /dev/null +++ b/mlartifacts/1/models/m-acf59e64090c4983aedf4f38e00e88d8/artifacts/requirements.txt @@ -0,0 +1,93 @@ +aiohappyeyeballs==2.6.2 +aiohttp==3.14.1 +aiosignal==1.4.0 +alembic==1.18.4 +annotated-doc==0.0.4 +annotated-types==0.7.0 +anyio==4.14.0 +attrs==26.1.0 +blinker==1.9.0 +cachetools==7.1.4 +certifi==2026.6.17 +cffi==2.0.0; platform_python_implementation != "PyPy" +charset-normalizer==3.4.7 +click==8.4.1 +cloudpickle==3.1.2 +colorama==0.4.6; sys_platform == "win32" +contourpy==1.3.3 +cryptography==48.0.1 +cycler==0.12.1 +databricks-sdk==0.118.0 +docker==7.1.0 +fastapi==0.138.0 +flask-cors==6.0.5 +flask==3.1.3 +fonttools==4.63.0 +frozenlist==1.8.0 +gitdb==4.0.12 +gitpython==3.1.50 +google-auth==2.55.0 +graphene==3.4.3 +graphql-core==3.2.11 +graphql-relay==3.2.0 +greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" +gunicorn==26.0.0; sys_platform != "win32" +h11==0.16.0 +huey==3.0.3 +idna==3.18 +importlib-metadata==9.0.0 +itsdangerous==2.2.0 +jinja2==3.1.6 +joblib==1.5.3 +kiwisolver==1.5.0 +mako==1.3.12 +markupsafe==3.0.3 +matplotlib==3.11.0 +mlflow-skinny==3.14.0 +mlflow-tracing==3.14.0 +mlflow==3.14.0 +multidict==6.7.1 +narwhals==2.22.1 +numpy==2.4.6 +opentelemetry-api==1.42.1 +opentelemetry-proto==1.42.1 +opentelemetry-sdk==1.42.1 +opentelemetry-semantic-conventions==0.63b1 +packaging==26.2 +pandas==2.3.3 +pillow==12.2.0 +prettytable==3.17.0 +propcache==0.5.2 +protobuf==6.33.6 +pyarrow==24.0.0 +pyasn1-modules==0.4.2 +pyasn1==0.6.3 +pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation != "PyPy" +pydantic-core==2.46.4 +pydantic==2.13.4 +pyparsing==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.2 +pytz==2026.2 +pywin32==312; sys_platform == "win32" +pyyaml==6.0.3 +requests==2.34.2 +scikit-learn==1.9.0 +scipy==1.18.0 +six==1.17.0 +skops==0.14.0 +smmap==5.0.3 +sqlalchemy==2.0.51 +sqlparse==0.5.5 +starlette==1.3.1 +threadpoolctl==3.6.0 +typing-extensions==4.15.0 +typing-inspection==0.4.2 +tzdata==2026.2 +urllib3==2.7.0 +uvicorn==0.49.0 +waitress==3.0.2; sys_platform == "win32" +wcwidth==0.8.1 +werkzeug==3.1.8 +yarl==1.24.2 +zipp==4.1.0 \ No newline at end of file diff --git a/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/.python-version b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/MLmodel b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/MLmodel new file mode 100644 index 0000000..f9c913b --- /dev/null +++ b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/MLmodel @@ -0,0 +1,45 @@ +artifact_path: mlflow-artifacts:/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts +flavors: + python_function: + artifacts: + model: + path: artifacts/isolation_forest.pkl + uri: /tmp/tmp7_w11p5z/isolation_forest.pkl + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-bd3c0c136325420f968da246eb391a3f +model_size_bytes: 1247806 +model_uuid: m-bd3c0c136325420f968da246eb391a3f +prompts: null +run_id: f127d3357648472a91f898dc506e2311 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}, {"type": "string", "name": "shap_values", "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-29 13:12:50.535445' diff --git a/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/artifacts/isolation_forest.pkl new file mode 100644 index 0000000000000000000000000000000000000000..c6e0da1b59c2fa424e99e02eb479b26c6acfae1f GIT binary patch literal 1244675 zcmd4430zIz*FSzul_V)sBxOiQN+L)9c0i3~TMZ_S$Q&z4mmEclTg7i7u?4e}0l~_6|0d zu1<yV|(92l@5Ub8~jEbhmSMn$3L@7&K;E(B>d{zn=7aJ4Z`* zXV;*4ej-lh?YC}02G)LJ?YHJGmadkLZb4)GdO2EpnY&p!x;WUl1^IQi^l*0$@)P;_ zaFvavy9b?NkPf_QlZoZmbEUJhyPLbKrAyErKk@dPU!;5ddOAC=G@oL9R2l+4xA$=bQ?jk`^d!B{^*PaD^j&TjNI%dfYUvy;1}qn%S* z3F+K-Y^cz=+;5{_UzEt)$`E}V8G;iKK{U7vS+kXb&9<*|g8LjocZZ>XibZu};YVKy|6!iNu 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aiohappyeyeballs==2.6.2 + - aiohttp==3.14.1 + - aiosignal==1.4.0 + - alembic==1.18.4 + - annotated-doc==0.0.4 + - annotated-types==0.7.0 + - anyio==4.14.0 + - attrs==26.1.0 + - blinker==1.9.0 + - cachetools==7.1.4 + - certifi==2026.6.17 + - cffi==2.0.0; platform_python_implementation != "PyPy" + - charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - llvmlite==0.45.1; platform_machine == "x86_64" and sys_platform == "darwin" + - llvmlite==0.47.0; platform_machine != "x86_64" or sys_platform != "darwin" + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numba==0.62.1; platform_machine == "x86_64" and sys_platform == "darwin" + - numba==0.65.1; platform_machine != "x86_64" or sys_platform != "darwin" + - numpy==2.3.5; platform_machine == "x86_64" and sys_platform == "darwin" + - numpy==2.4.6; platform_machine != "x86_64" or sys_platform != "darwin" + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - shap==0.52.0 + - six==1.17.0 + - skops==0.14.0 + - slicer==0.0.8 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - tqdm==4.68.3 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 + - waitress==3.0.2; sys_platform == "win32" + - wcwidth==0.8.1 + - werkzeug==3.1.8 + - yarl==1.24.2 + - zipp==4.1.0 +name: mlflow-env diff --git a/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/pyproject.toml b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/pyproject.toml new file mode 100644 index 0000000..44ca86f --- /dev/null +++ b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "fraud-models" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mlflow>=3.14.0", + "pandas>=2.3.3", + "scikit-learn>=1.9.0", + "shap>=0.52.0", +] diff --git a/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/python_env.yaml b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-bd3c0c136325420f968da246eb391a3f/artifacts/python_env.yaml @@ -0,0 +1,7 @@ +python: 3.13.13 +build_dependencies: +- pip +- setuptools +- wheel +dependencies: 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b/mlartifacts/1/models/m-cc16c062c0cc49fd97d4912bfebb0e75/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-cc16c062c0cc49fd97d4912bfebb0e75/artifacts/MLmodel b/mlartifacts/1/models/m-cc16c062c0cc49fd97d4912bfebb0e75/artifacts/MLmodel new file mode 100644 index 0000000..d47840a --- /dev/null +++ b/mlartifacts/1/models/m-cc16c062c0cc49fd97d4912bfebb0e75/artifacts/MLmodel @@ -0,0 +1,54 @@ +artifact_path: mlflow-artifacts:/1/models/m-cc16c062c0cc49fd97d4912bfebb0e75/artifacts +flavors: + python_function: + artifacts: + input_dim: + path: artifacts/input_dim.txt + uri: /tmp/tmpp1ktwuo_/input_dim.txt + scaler: + path: artifacts/scaler.pkl + uri: /tmp/tmpp1ktwuo_/scaler.pkl + threshold: + path: artifacts/threshold.txt + uri: /tmp/tmpp1ktwuo_/threshold.txt + weights: + path: artifacts/model.pt + uri: /tmp/tmpp1ktwuo_/model.pt + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-cc16c062c0cc49fd97d4912bfebb0e75 +model_size_bytes: 41835 +model_uuid: m-cc16c062c0cc49fd97d4912bfebb0e75 +prompts: null +run_id: 9b3eca8de2d8451da99d33b0569a828d +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "float", + "name": "anomaly_score", "required": true}, {"type": "float", "name": "threshold", + "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-07-01 08:00:57.265291' diff --git 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b/mlartifacts/1/models/m-d013dfe57f234ba789bd01b3eea28057/artifacts/requirements.txt @@ -0,0 +1,93 @@ +aiohappyeyeballs==2.6.2 +aiohttp==3.14.1 +aiosignal==1.4.0 +alembic==1.18.4 +annotated-doc==0.0.4 +annotated-types==0.7.0 +anyio==4.14.0 +attrs==26.1.0 +blinker==1.9.0 +cachetools==7.1.4 +certifi==2026.6.17 +cffi==2.0.0; platform_python_implementation != "PyPy" +charset-normalizer==3.4.7 +click==8.4.1 +cloudpickle==3.1.2 +colorama==0.4.6; sys_platform == "win32" +contourpy==1.3.3 +cryptography==48.0.1 +cycler==0.12.1 +databricks-sdk==0.118.0 +docker==7.1.0 +fastapi==0.138.0 +flask-cors==6.0.5 +flask==3.1.3 +fonttools==4.63.0 +frozenlist==1.8.0 +gitdb==4.0.12 +gitpython==3.1.50 +google-auth==2.55.0 +graphene==3.4.3 +graphql-core==3.2.11 +graphql-relay==3.2.0 +greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" +gunicorn==26.0.0; sys_platform != "win32" +h11==0.16.0 +huey==3.0.3 +idna==3.18 +importlib-metadata==9.0.0 +itsdangerous==2.2.0 +jinja2==3.1.6 +joblib==1.5.3 +kiwisolver==1.5.0 +mako==1.3.12 +markupsafe==3.0.3 +matplotlib==3.11.0 +mlflow-skinny==3.14.0 +mlflow-tracing==3.14.0 +mlflow==3.14.0 +multidict==6.7.1 +narwhals==2.22.1 +numpy==2.4.6 +opentelemetry-api==1.42.1 +opentelemetry-proto==1.42.1 +opentelemetry-sdk==1.42.1 +opentelemetry-semantic-conventions==0.63b1 +packaging==26.2 +pandas==2.3.3 +pillow==12.2.0 +prettytable==3.17.0 +propcache==0.5.2 +protobuf==6.33.6 +pyarrow==24.0.0 +pyasn1-modules==0.4.2 +pyasn1==0.6.3 +pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation != "PyPy" +pydantic-core==2.46.4 +pydantic==2.13.4 +pyparsing==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.2 +pytz==2026.2 +pywin32==312; sys_platform == "win32" +pyyaml==6.0.3 +requests==2.34.2 +scikit-learn==1.9.0 +scipy==1.18.0 +six==1.17.0 +skops==0.14.0 +smmap==5.0.3 +sqlalchemy==2.0.51 +sqlparse==0.5.5 +starlette==1.3.1 +threadpoolctl==3.6.0 +typing-extensions==4.15.0 +typing-inspection==0.4.2 +tzdata==2026.2 +urllib3==2.7.0 +uvicorn==0.49.0 +waitress==3.0.2; sys_platform == "win32" +wcwidth==0.8.1 +werkzeug==3.1.8 +yarl==1.24.2 +zipp==4.1.0 \ No newline at end of file diff --git a/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/.python-version b/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/MLmodel b/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/MLmodel new file mode 100644 index 0000000..0331ba9 --- /dev/null +++ b/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/MLmodel @@ -0,0 +1,45 @@ +artifact_path: mlflow-artifacts:/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts +flavors: + python_function: + artifacts: + model: + path: artifacts/isolation_forest.pkl + uri: /tmp/tmp1xyv_a_n/isolation_forest.pkl + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-d46191ce42ad4c1a898fbbaf53b7054a +model_size_bytes: 639596 +model_uuid: m-d46191ce42ad4c1a898fbbaf53b7054a +prompts: null +run_id: 0ff8b58906e74c7dbc8d3a7291e5c384 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}, {"type": "string", "name": "shap_values", "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-27 13:56:28.625932' diff --git a/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-d46191ce42ad4c1a898fbbaf53b7054a/artifacts/artifacts/isolation_forest.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b6d57f0458929485660ee20a0069f39ff1e22fb9 GIT binary patch literal 636465 zcmdpf30RHW_y36yiG<3OibhFDlC*ad%|a1M8WpKDkolMrijXN&NK(=~s!k~s$&}`K zAd<{7|KB?AI^Em3@9&&%-Fw~V?|JUsz3Xd5?Jy;h}Z>^B?88 z#>K(L!&TA2)ze|Et&4-AwX>s}hl8hAkpED9Pd67EFK0K`xy(05gXDGuZ4Hw3A4vS} zyw=9c%_GRff3T}H>(MjF(B5B=^=R#G<6*PbGf2*#f31y=wWrNmcNYiGAb(yPZ!fnX 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b/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/MLmodel @@ -0,0 +1,43 @@ +artifact_path: mlflow-artifacts:/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts +flavors: + python_function: + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.sklearn + model_path: model.skops + predict_fn: predict + python_version: 3.13.13 + sklearn: + code: null + pickled_model: model.skops + serialization_format: skops + sklearn_version: 1.9.0 + skops_trusted_types: null +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-e20cf041c120422dbf613df9850d7715 +model_size_bytes: 2339226 +model_uuid: m-e20cf041c120422dbf613df9850d7715 +prompts: null +run_id: 6ae6caee46944d8fa5d0d7ab48899318 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-22 09:35:37.543241' diff --git a/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/conda.yaml b/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/conda.yaml new file mode 100644 index 0000000..a6bbe50 --- /dev/null +++ b/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/conda.yaml @@ -0,0 +1,103 @@ +channels: +- conda-forge +dependencies: +- python=3.13.13 +- pip +- pip: + - aiohappyeyeballs==2.6.2 + - aiohttp==3.14.1 + - aiosignal==1.4.0 + - alembic==1.18.4 + - annotated-doc==0.0.4 + - annotated-types==0.7.0 + - anyio==4.14.0 + - attrs==26.1.0 + - blinker==1.9.0 + - cachetools==7.1.4 + - certifi==2026.6.17 + - cffi==2.0.0; platform_python_implementation != "PyPy" + - charset-normalizer==3.4.7 + - click==8.4.1 + - cloudpickle==3.1.2 + - colorama==0.4.6; sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - 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a/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/requirements.txt b/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/requirements.txt new file mode 100644 index 0000000..8b56ecc --- /dev/null +++ b/mlartifacts/1/models/m-e20cf041c120422dbf613df9850d7715/artifacts/requirements.txt @@ -0,0 +1,93 @@ +aiohappyeyeballs==2.6.2 +aiohttp==3.14.1 +aiosignal==1.4.0 +alembic==1.18.4 +annotated-doc==0.0.4 +annotated-types==0.7.0 +anyio==4.14.0 +attrs==26.1.0 +blinker==1.9.0 +cachetools==7.1.4 +certifi==2026.6.17 +cffi==2.0.0; platform_python_implementation != "PyPy" +charset-normalizer==3.4.7 +click==8.4.1 +cloudpickle==3.1.2 +colorama==0.4.6; sys_platform == "win32" +contourpy==1.3.3 +cryptography==48.0.1 +cycler==0.12.1 +databricks-sdk==0.118.0 +docker==7.1.0 +fastapi==0.138.0 +flask-cors==6.0.5 +flask==3.1.3 +fonttools==4.63.0 +frozenlist==1.8.0 +gitdb==4.0.12 +gitpython==3.1.50 +google-auth==2.55.0 +graphene==3.4.3 +graphql-core==3.2.11 +graphql-relay==3.2.0 +greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" +gunicorn==26.0.0; sys_platform != "win32" +h11==0.16.0 +huey==3.0.3 +idna==3.18 +importlib-metadata==9.0.0 +itsdangerous==2.2.0 +jinja2==3.1.6 +joblib==1.5.3 +kiwisolver==1.5.0 +mako==1.3.12 +markupsafe==3.0.3 +matplotlib==3.11.0 +mlflow-skinny==3.14.0 +mlflow-tracing==3.14.0 +mlflow==3.14.0 +multidict==6.7.1 +narwhals==2.22.1 +numpy==2.4.6 +opentelemetry-api==1.42.1 +opentelemetry-proto==1.42.1 +opentelemetry-sdk==1.42.1 +opentelemetry-semantic-conventions==0.63b1 +packaging==26.2 +pandas==2.3.3 +pillow==12.2.0 +prettytable==3.17.0 +propcache==0.5.2 +protobuf==6.33.6 +pyarrow==24.0.0 +pyasn1-modules==0.4.2 +pyasn1==0.6.3 +pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation != "PyPy" +pydantic-core==2.46.4 +pydantic==2.13.4 +pyparsing==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.2 +pytz==2026.2 +pywin32==312; sys_platform == "win32" +pyyaml==6.0.3 +requests==2.34.2 +scikit-learn==1.9.0 +scipy==1.18.0 +six==1.17.0 +skops==0.14.0 +smmap==5.0.3 +sqlalchemy==2.0.51 +sqlparse==0.5.5 +starlette==1.3.1 +threadpoolctl==3.6.0 +typing-extensions==4.15.0 +typing-inspection==0.4.2 +tzdata==2026.2 +urllib3==2.7.0 +uvicorn==0.49.0 +waitress==3.0.2; sys_platform == "win32" +wcwidth==0.8.1 +werkzeug==3.1.8 +yarl==1.24.2 +zipp==4.1.0 \ No newline at end of file diff --git a/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/.python-version b/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/MLmodel b/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/MLmodel new file mode 100644 index 0000000..dc53d44 --- /dev/null +++ b/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/MLmodel @@ -0,0 +1,45 @@ +artifact_path: mlflow-artifacts:/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts +flavors: + python_function: + artifacts: + model: + path: artifacts/isolation_forest.pkl + uri: /tmp/tmps3v53unb/isolation_forest.pkl + cloudpickle_version: 3.1.2 + code: null + env: + conda: conda.yaml + virtualenv: python_env.yaml + loader_module: mlflow.pyfunc.model + python_model: python_model.pkl + python_version: 3.13.13 + streamable: false +is_signature_from_type_hint: false +mlflow_version: 3.14.0 +model_id: m-e90e833f4da747039bbe21952ab9aa61 +model_size_bytes: 639262 +model_uuid: m-e90e833f4da747039bbe21952ab9aa61 +prompts: null +run_id: 164a8d06357a4869aa3c7f653e74a796 +signature: + inputs: '[{"type": "double", "name": "session_start_sin", "required": true}, {"type": + "double", "name": "session_start_cos", "required": true}, {"type": "double", "name": + "session_end_sin", "required": true}, {"type": "double", "name": "session_end_cos", + "required": true}, {"type": "double", "name": "event_count", "required": true}, + {"type": "double", "name": "unique_event_types", "required": true}, {"type": "double", + "name": "screens_visited", "required": true}, {"type": "double", "name": "duration_seconds", + "required": true}, {"type": "double", "name": "events_per_minute", "required": + true}, {"type": "double", "name": "inter_event_time_mean_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_median_seconds", "required": + true}, {"type": "double", "name": "inter_event_time_std_seconds", "required": + true}, {"type": "double", "name": "max_inter_event_gap_seconds", "required": true}, + {"type": "double", "name": "session_start_hour", "required": true}, {"type": "double", + "name": "session_day_of_week", "required": true}, {"type": "double", "name": "session_day_of_week_sin", + "required": true}, {"type": "double", "name": "session_day_of_week_cos", "required": + true}, {"type": "double", "name": "distinct_hours_active", "required": true}]' + outputs: '[{"type": "long", "name": "prediction", "required": true}, {"type": "double", + "name": "anomaly_score", "required": true}, {"type": "double", "name": "decision_score", + "required": true}]' + params: null +type_hint_from_example: false +utc_time_created: '2026-06-22 14:31:09.557211' diff --git a/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/artifacts/isolation_forest.pkl b/mlartifacts/1/models/m-e90e833f4da747039bbe21952ab9aa61/artifacts/artifacts/isolation_forest.pkl new file mode 100644 index 0000000000000000000000000000000000000000..01619a3ec2c3347df302641120c226142a95f544 GIT binary patch literal 636493 zcmdpf30RHW_y36yiG<3OibhFDlC*ad%|a1M8WpKDkolMrijXN&NK(=~s!k~s$&}`K zAd<{7|KB?AI^Em3@9&&%-Fw~V?|JUsz3Xd5>)w!b9u$=ReAG 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sys_platform == "win32" + - contourpy==1.3.3 + - cryptography==48.0.1 + - cycler==0.12.1 + - databricks-sdk==0.118.0 + - docker==7.1.0 + - fastapi==0.138.0 + - flask-cors==6.0.5 + - flask==3.1.3 + - fonttools==4.63.0 + - frozenlist==1.8.0 + - gitdb==4.0.12 + - gitpython==3.1.50 + - google-auth==2.55.0 + - graphene==3.4.3 + - graphql-core==3.2.11 + - graphql-relay==3.2.0 + - greenlet==3.5.2; platform_machine == "AMD64" or platform_machine == "WIN32" or + platform_machine == "aarch64" or platform_machine == "amd64" or platform_machine + == "ppc64le" or platform_machine == "win32" or platform_machine == "x86_64" + - gunicorn==26.0.0; sys_platform != "win32" + - h11==0.16.0 + - huey==3.0.3 + - idna==3.18 + - importlib-metadata==9.0.0 + - itsdangerous==2.2.0 + - jinja2==3.1.6 + - joblib==1.5.3 + - kiwisolver==1.5.0 + - mako==1.3.12 + - markupsafe==3.0.3 + - matplotlib==3.11.0 + - mlflow-skinny==3.14.0 + - mlflow-tracing==3.14.0 + - mlflow==3.14.0 + - multidict==6.7.1 + - narwhals==2.22.1 + - numpy==2.4.6 + - opentelemetry-api==1.42.1 + - opentelemetry-proto==1.42.1 + - opentelemetry-sdk==1.42.1 + - opentelemetry-semantic-conventions==0.63b1 + - packaging==26.2 + - pandas==2.3.3 + - pillow==12.2.0 + - prettytable==3.17.0 + - propcache==0.5.2 + - protobuf==6.33.6 + - pyarrow==24.0.0 + - pyasn1-modules==0.4.2 + - pyasn1==0.6.3 + - pycparser==3.0; implementation_name != "PyPy" and platform_python_implementation + != "PyPy" + - pydantic-core==2.46.4 + - pydantic==2.13.4 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.2.2 + - pytz==2026.2 + - pywin32==312; sys_platform == "win32" + - pyyaml==6.0.3 + - requests==2.34.2 + - scikit-learn==1.9.0 + - scipy==1.18.0 + - six==1.17.0 + - skops==0.14.0 + - smmap==5.0.3 + - sqlalchemy==2.0.51 + - sqlparse==0.5.5 + - starlette==1.3.1 + - threadpoolctl==3.6.0 + - typing-extensions==4.15.0 + - typing-inspection==0.4.2 + - tzdata==2026.2 + - urllib3==2.7.0 + - uvicorn==0.49.0 + - waitress==3.0.2; sys_platform == "win32" + - wcwidth==0.8.1 + - werkzeug==3.1.8 + - yarl==1.24.2 + - zipp==4.1.0 +name: mlflow-env diff --git a/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/pyproject.toml b/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/pyproject.toml new file mode 100644 index 0000000..6f215a5 --- /dev/null +++ b/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "fraud-models" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mlflow>=3.14.0", + "pandas>=2.3.3", + "scikit-learn>=1.9.0", +] diff --git a/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/python_env.yaml b/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/python_env.yaml new file mode 100644 index 0000000..dbbc50f --- /dev/null +++ b/mlartifacts/1/models/m-f7780079a9224f0caac4d322798574a3/artifacts/python_env.yaml @@ -0,0 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= "fraud-models" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mlflow>=3.14.0", + "pandas>=2.3.3", + "scikit-learn>=1.9.0", + "shap>=0.52.0", +] diff --git a/test.py b/test.py new file mode 100644 index 0000000..34354a9 --- /dev/null +++ b/test.py @@ -0,0 +1,48 @@ +import json +import requests +import pandas as pd + +# Create a tiny mock dataframe matching your query's feature layout +# Ensure the columns precisely match the ones your model trained on! +mock_batch = pd.DataFrame([{ + 'person_id': 'usr_123456', + 'session_id': 'sess_normal_001', + + 'session_start': '2026-06-08 19:18:42', + 'session_start_sin': -0.9659, + 'session_start_cos': 0.2588, + 'session_end': '2026-06-08 19:29:58', + 'session_end_sin': -0.8746, + 'session_end_cos': 0.4848, + + 'event_count': 26, + 'unique_event_types': 9, + 'screens_visited': 11, + 'duration_seconds': 676, + 'events_per_minute': 5.15, + + 'inter_event_time_mean_seconds': 11.7, + 'inter_event_time_median_seconds': 8.4, + 'inter_event_time_std_seconds': 10.6, + 'max_inter_event_gap_seconds': 74.2, + + 'session_start_hour': 19, + 'session_day_of_week': 0, + 'session_day_of_week_sin': 0.0, + 'session_day_of_week_cos': 1.0, + + 'distinct_hours_active': 1 +}]) + +# Format into MLflow's split layout +payload = {"dataframe_split": mock_batch.to_dict(orient="split")} + +# Hit the local model server +response = requests.post( + "http://127.0.0.1:5001/invocations", + data=json.dumps(payload), + headers={"Content-Type": "application/json"} +) + +print("Server Response Status:", response.status_code) +print("Predictions (1=Clean, -1=Anomaly):", response.json()) \ No newline at end of file diff --git a/train_and_register_autoencoder.py b/train_and_register_autoencoder.py new file mode 100644 index 0000000..1e121de --- /dev/null +++ b/train_and_register_autoencoder.py @@ -0,0 +1,374 @@ +import os +import requests +import pandas as pd +import mlflow +import mlflow.data +import mlflow.pyfunc +from mlflow.models.signature import infer_signature +import pickle +import tempfile +import numpy as np +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, TensorDataset +import joblib# Configuration +PERSONAL_API_KEY = os.getenv("PERSONAL_API_KEY") +POSTHOG_PROJECT_ID = os.getenv("POSTHOG_PROJECT_ID") +POSTHOG_HOST = os.getenv("POSTHOG_HOST") + +def load_query_template(filepath: str, start_date: str, end_date: str) -> str: + """Reads SQL file and replaces {start_date}/{end_date} with formatted strings.""" + with open(filepath, 'r') as f: + template = f.read() + return template.format(start_date=start_date, end_date=end_date) + +def fetch_posthog_data(query_string: str) -> pd.DataFrame: + """Executes HogQL/ClickHouse query on the PostHog API.""" + response = requests.post( + f"{POSTHOG_HOST}/api/projects/{POSTHOG_PROJECT_ID}/query/", + headers={ + "Authorization": f"Bearer {PERSONAL_API_KEY}", + "Content-Type": "application/json", + }, + json={ + "query": {"kind": "HogQLQuery", "query": query_string}, + }, + ) + response.raise_for_status() + data = response.json() + return pd.DataFrame(data=data['results'], columns=data['columns']) + +class Autoencoder(nn.Module): + def __init__(self, input_dim): + super().__init__() + + self.encoder = nn.Sequential( + nn.Linear(input_dim, 64), + nn.ReLU(), + nn.Linear(64, 32), + nn.ReLU(), + nn.Linear(32, 16), + nn.ReLU(), + ) + + self.decoder = nn.Sequential( + nn.Linear(16, 32), + nn.ReLU(), + nn.Linear(32, 64), + nn.ReLU(), + nn.Linear(64, input_dim), + ) + + def forward(self, x): + z = self.encoder(x) + return self.decoder(z) + +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split + + +class AutoencoderWithScores(mlflow.pyfunc.PythonModel): + def load_context(self, context): + self.scaler = joblib.load(context.artifacts["scaler"]) + + with open(context.artifacts["input_dim"], "r") as f: + input_dim = int(f.read()) + + self.model = Autoencoder(input_dim) + self.model.load_state_dict( + torch.load(context.artifacts["weights"], map_location="cpu") + ) + self.model.eval() + + with open(context.artifacts["threshold"], "r") as f: + self.threshold = float(f.read()) + + def predict(self, context, model_input): + X = self.scaler.transform(model_input) + X = X.astype(np.float32) + + with torch.inference_mode(): + reconstructed = self.model(torch.from_numpy(X)).numpy() + + reconstruction_error = np.mean( + (X - reconstructed) ** 2, + axis=1, + ) + + predictions = np.where( + reconstruction_error > self.threshold, + -1, + 1, + ) + + return pd.DataFrame( + { + "prediction": predictions, + "anomaly_score": reconstruction_error, + "threshold": self.threshold, + } + ) + + +def run_training_pipeline(): + mlflow.set_tracking_uri("http://127.0.0.1:8000") + mlflow.set_experiment("Session_Fraud_Detection") + + sql_filepath = "./fetch_clean_sessions.sql" + + start_date = "2026-03-21 00:00:00" + end_date = "2026-04-21 00:00:00" + + populated_query = load_query_template( + sql_filepath, + start_date=start_date, + end_date=end_date, + ) + + print("Fetching training baseline from PostHog...") + + df_raw = fetch_posthog_data(populated_query) + + print(len(df_raw), "rows fetched from PostHog.") + + start_date = "2026-04-21 00:00:01" + end_date = "2026-05-21 00:00:00" + + populated_query = load_query_template( + sql_filepath, + start_date=start_date, + end_date=end_date, + ) + + df_raw_2 = fetch_posthog_data(populated_query) + + print(len(df_raw_2), "rows fetched from PostHog.") + + df_raw = pd.concat( + [df_raw, df_raw_2], + ignore_index=True, + ) + + feature_cols = [ + c + for c in df_raw.columns + if c not in [ + "person_id", + "session_id", + "session_start", + "session_end", + ] + ] + + df_features = df_raw[feature_cols].astype(float) + + with mlflow.start_run(run_name="PostHog_Autoencoder_Training"): + + scaler = StandardScaler() + + X = scaler.fit_transform(df_features).astype(np.float32) + + X_train, X_val = train_test_split( + X, + test_size=0.2, + random_state=42, + ) + + train_loader = DataLoader( + TensorDataset(torch.from_numpy(X_train)), + batch_size=256, + shuffle=True, + ) + + model = Autoencoder(X.shape[1]) + + optimizer = torch.optim.Adam( + model.parameters(), + lr=1e-3, + ) + + criterion = nn.MSELoss() + + best_loss = float("inf") + patience = 10 + patience_counter = 0 + + for epoch in range(1000): + + model.train() + + train_loss = 0.0 + + for (batch,) in train_loader: + + optimizer.zero_grad() + + reconstructed = model(batch) + + loss = criterion(reconstructed, batch) + + loss.backward() + + optimizer.step() + + train_loss += loss.item() + + train_loss /= len(train_loader) + + model.eval() + + with torch.inference_mode(): + + val_tensor = torch.from_numpy(X_val) + + val_reconstruction = model(val_tensor) + + val_loss = criterion( + val_reconstruction, + val_tensor, + ).item() + + print( + f"Epoch {epoch+1} | " + f"Train: {train_loss:.6f} | " + f"Val: {val_loss:.6f}" + ) + + if val_loss < best_loss: + + best_loss = val_loss + + best_state = { + k: v.cpu().clone() + for k, v in model.state_dict().items() + } + + patience_counter = 0 + + else: + + patience_counter += 1 + + if patience_counter >= patience: + print("Early stopping.") + break + + model.load_state_dict(best_state) + + model.eval() + + with torch.inference_mode(): + + val_reconstructed = model( + torch.from_numpy(X_val) + ).numpy() + + reconstruction_error = np.mean( + (X_val - val_reconstructed) ** 2, + axis=1, + ) + + threshold = np.percentile( + reconstruction_error, + 99, + ) + + example_input = df_features.head(1) + + example_scaled = scaler.transform(example_input).astype(np.float32) + + with torch.inference_mode(): + + reconstructed = model( + torch.from_numpy(example_scaled) + ).numpy() + + example_error = np.mean( + (example_scaled - reconstructed) ** 2, + axis=1, + ) + + example_output = pd.DataFrame( + { + "prediction": np.where( + example_error > threshold, + -1, + 1, + ), + "anomaly_score": example_error, + "threshold": threshold, + } + ) + + signature = infer_signature( + example_input, + example_output, + ) + + mlflow.log_params( + { + "architecture": "64-32-16-32-64", + "optimizer": "Adam", + "learning_rate": 1e-3, + "epochs": epoch + 1, + "batch_size": 256, + "threshold_percentile": 99, + } + ) + + with tempfile.TemporaryDirectory() as tmpdir: + + scaler_path = os.path.join( + tmpdir, + "scaler.pkl", + ) + + weights_path = os.path.join( + tmpdir, + "model.pt", + ) + + threshold_path = os.path.join( + tmpdir, + "threshold.txt", + ) + + input_dim_path = os.path.join( + tmpdir, + "input_dim.txt", + ) + + joblib.dump( + scaler, + scaler_path, + ) + + torch.save( + model.state_dict(), + weights_path, + ) + + with open(threshold_path, "w") as f: + f.write(str(threshold)) + + with open(input_dim_path, "w") as f: + f.write(str(X.shape[1])) + + mlflow.pyfunc.log_model( + artifact_path="autoencoder_model", + python_model=AutoencoderWithScores(), + artifacts={ + "weights": weights_path, + "scaler": scaler_path, + "threshold": threshold_path, + "input_dim": input_dim_path, + }, + signature=signature, + registered_model_name="Autoencoder_Anomaly_Detector", + ) + + print("Model tracking and registry entry complete.") + + +if __name__ == "__main__": + run_training_pipeline() \ No newline at end of file diff --git a/train_and_register_forest.py b/train_and_register_forest.py new file mode 100644 index 0000000..06b6b4c --- /dev/null +++ b/train_and_register_forest.py @@ -0,0 +1,374 @@ +import os +import requests +import pandas as pd +import mlflow +import mlflow.data +import mlflow.pyfunc +from mlflow.models.signature import infer_signature +import pickle +import tempfile +import numpy as np +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, TensorDataset +import joblib# Configuration +PERSONAL_API_KEY = os.getenv("PERSONAL_API_KEY") +POSTHOG_PROJECT_ID = os.getenv("POSTHOG_PROJECT_ID") +POSTHOG_HOST = os.getenv("POSTHOG_HOST") + +def load_query_template(filepath: str, start_date: str, end_date: str) -> str: + """Reads SQL file and replaces {start_date}/{end_date} with formatted strings.""" + with open(filepath, 'r') as f: + template = f.read() + return template.format(start_date=start_date, end_date=end_date) + +def fetch_posthog_data(query_string: str) -> pd.DataFrame: + """Executes HogQL/ClickHouse query on the PostHog API.""" + response = requests.post( + f"{POSTHOG_HOST}/api/projects/{POSTHOG_PROJECT_ID}/query/", + headers={ + "Authorization": f"Bearer {PERSONAL_API_KEY}", + "Content-Type": "application/json", + }, + json={ + "query": {"kind": "HogQLQuery", "query": query_string}, + }, + ) + response.raise_for_status() + data = response.json() + return pd.DataFrame(data=data['results'], columns=data['columns']) + +class Autoencoder(nn.Module): + def __init__(self, input_dim): + super().__init__() + + self.encoder = nn.Sequential( + nn.Linear(input_dim, 64), + nn.ReLU(), + nn.Linear(64, 32), + nn.ReLU(), + nn.Linear(32, 16), + nn.ReLU(), + ) + + self.decoder = nn.Sequential( + nn.Linear(16, 32), + nn.ReLU(), + nn.Linear(32, 64), + nn.ReLU(), + nn.Linear(64, input_dim), + ) + + def forward(self, x): + z = self.encoder(x) + return self.decoder(z) + +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split + + +class AutoencoderWithScores(mlflow.pyfunc.PythonModel): + def load_context(self, context): + self.scaler = joblib.load(context.artifacts["scaler"]) + + with open(context.artifacts["input_dim"], "r") as f: + input_dim = int(f.read()) + + self.model = Autoencoder(input_dim) + self.model.load_state_dict( + torch.load(context.artifacts["weights"], map_location="cpu") + ) + self.model.eval() + + with open(context.artifacts["threshold"], "r") as f: + self.threshold = float(f.read()) + + def predict(self, context, model_input): + X = self.scaler.transform(model_input) + X = X.astype(np.float32) + + with torch.inference_mode(): + reconstructed = self.model(torch.from_numpy(X)).numpy() + + reconstruction_error = np.mean( + (X - reconstructed) ** 2, + axis=1, + ) + + predictions = np.where( + reconstruction_error > self.threshold, + -1, + 1, + ) + + return pd.DataFrame( + { + "prediction": predictions, + "anomaly_score": reconstruction_error, + "threshold": self.threshold, + } + ) + + +def run_training_pipeline(): + mlflow.set_tracking_uri("http://127.0.0.1:8000") + mlflow.set_experiment("Session_Fraud_Detection") + + sql_filepath = "./fetch_clean_sessions.sql" + + start_date = "2026-03-21 00:00:00" + end_date = "2026-04-21 00:00:00" + + populated_query = load_query_template( + sql_filepath, + start_date=start_date, + end_date=end_date, + ) + + print("Fetching training baseline from PostHog...") + + df_raw = fetch_posthog_data(populated_query) + + print(len(df_raw), "rows fetched from PostHog.") + + start_date = "2026-04-21 00:00:01" + end_date = "2026-05-21 00:00:00" + + populated_query = load_query_template( + sql_filepath, + start_date=start_date, + end_date=end_date, + ) + + df_raw_2 = fetch_posthog_data(populated_query) + + print(len(df_raw_2), "rows fetched from PostHog.") + + df_raw = pd.concat( + [df_raw, df_raw_2], + ignore_index=True, + ) + + feature_cols = [ + c + for c in df_raw.columns + if c not in [ + "person_id", + "session_id", + "session_start", + "session_end", + ] + ] + + df_features = df_raw[feature_cols].astype(float) + + with mlflow.start_run(run_name="PostHog_Autoencoder_Training"): + + scaler = StandardScaler() + + X = scaler.fit_transform(df_features).astype(np.float32) + + X_train, X_val = train_test_split( + X, + test_size=0.2, + random_state=42, + ) + + train_loader = DataLoader( + TensorDataset(torch.from_numpy(X_train)), + batch_size=256, + shuffle=True, + ) + + model = Autoencoder(X.shape[1]) + + optimizer = torch.optim.Adam( + model.parameters(), + lr=1e-3, + ) + + criterion = nn.MSELoss() + + best_loss = float("inf") + patience = 10 + patience_counter = 0 + + for epoch in range(100): + + model.train() + + train_loss = 0.0 + + for (batch,) in train_loader: + + optimizer.zero_grad() + + reconstructed = model(batch) + + loss = criterion(reconstructed, batch) + + loss.backward() + + optimizer.step() + + train_loss += loss.item() + + train_loss /= len(train_loader) + + model.eval() + + with torch.inference_mode(): + + val_tensor = torch.from_numpy(X_val) + + val_reconstruction = model(val_tensor) + + val_loss = criterion( + val_reconstruction, + val_tensor, + ).item() + + print( + f"Epoch {epoch+1} | " + f"Train: {train_loss:.6f} | " + f"Val: {val_loss:.6f}" + ) + + if val_loss < best_loss: + + best_loss = val_loss + + best_state = { + k: v.cpu().clone() + for k, v in model.state_dict().items() + } + + patience_counter = 0 + + else: + + patience_counter += 1 + + if patience_counter >= patience: + print("Early stopping.") + break + + model.load_state_dict(best_state) + + model.eval() + + with torch.inference_mode(): + + val_reconstructed = model( + torch.from_numpy(X_val) + ).numpy() + + reconstruction_error = np.mean( + (X_val - val_reconstructed) ** 2, + axis=1, + ) + + threshold = np.percentile( + reconstruction_error, + 99, + ) + + example_input = df_features.head(1) + + example_scaled = scaler.transform(example_input).astype(np.float32) + + with torch.inference_mode(): + + reconstructed = model( + torch.from_numpy(example_scaled) + ).numpy() + + example_error = np.mean( + (example_scaled - reconstructed) ** 2, + axis=1, + ) + + example_output = pd.DataFrame( + { + "prediction": np.where( + example_error > threshold, + -1, + 1, + ), + "anomaly_score": example_error, + "threshold": threshold, + } + ) + + signature = infer_signature( + example_input, + example_output, + ) + + mlflow.log_params( + { + "architecture": "64-32-16-32-64", + "optimizer": "Adam", + "learning_rate": 1e-3, + "epochs": epoch + 1, + "batch_size": 256, + "threshold_percentile": 99, + } + ) + + with tempfile.TemporaryDirectory() as tmpdir: + + scaler_path = os.path.join( + tmpdir, + "scaler.pkl", + ) + + weights_path = os.path.join( + tmpdir, + "model.pt", + ) + + threshold_path = os.path.join( + tmpdir, + "threshold.txt", + ) + + input_dim_path = os.path.join( + tmpdir, + "input_dim.txt", + ) + + joblib.dump( + scaler, + scaler_path, + ) + + torch.save( + model.state_dict(), + weights_path, + ) + + with open(threshold_path, "w") as f: + f.write(str(threshold)) + + with open(input_dim_path, "w") as f: + f.write(str(X.shape[1])) + + mlflow.pyfunc.log_model( + artifact_path="autoencoder_model", + python_model=AutoencoderWithScores(), + artifacts={ + "weights": weights_path, + "scaler": scaler_path, + "threshold": threshold_path, + "input_dim": input_dim_path, + }, + signature=signature, + registered_model_name="Autoencoder_Anomaly_Detector", + ) + + print("Model tracking and registry entry complete.") + + +if __name__ == "__main__": + run_training_pipeline() \ No newline at end of file diff --git a/train_and_register_nusvm.py b/train_and_register_nusvm.py new file mode 100644 index 0000000..9e6c6d2 --- /dev/null +++ b/train_and_register_nusvm.py @@ -0,0 +1,173 @@ +import os +import requests +import pandas as pd +import mlflow +import mlflow.data +import mlflow.pyfunc +from mlflow.models.signature import infer_signature +from sklearn.svm import OneClassSVM +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline +from datetime import datetime + +# Configuration +PERSONAL_API_KEY = os.getenv("PERSONAL_API_KEY") +POSTHOG_PROJECT_ID = os.getenv("POSTHOG_PROJECT_ID") +POSTHOG_HOST = os.getenv("POSTHOG_HOST") + +def load_query_template(filepath: str, start_date: str, end_date: str) -> str: + """Reads SQL file and replaces {start_date}/{end_date} with formatted strings.""" + with open(filepath, 'r') as f: + template = f.read() + return template.format(start_date=start_date, end_date=end_date) + +def fetch_posthog_data(query_string: str) -> pd.DataFrame: + """Executes HogQL/ClickHouse query on the PostHog API.""" + response = requests.post( + f"{POSTHOG_HOST}/api/projects/{POSTHOG_PROJECT_ID}/query/", + headers={ + "Authorization": f"Bearer {PERSONAL_API_KEY}", + "Content-Type": "application/json", + }, + json={ + "query": { + "kind": "HogQLQuery", + "query": query_string, + } + }, + ) + response.raise_for_status() + data = response.json() + + # PostHog query API responses return arrays for rows ('results') and column names ('columns') + df = pd.DataFrame(data=data['results'], columns=data['columns']) + return df + +def run_training_pipeline(): + mlflow.set_tracking_uri("http://127.0.0.1:8080") + mlflow.set_experiment("Session_Fraud_Detection") + + # 1. Parse your template file + sql_filepath = "./fetch_clean_sessions.sql" + start_date = "2026-03-21 00:00:00" + end_date = "2026-04-21 00:00:00" + populated_query = load_query_template(sql_filepath, start_date=start_date, end_date=end_date) + + # 2. Ingest your baseline from PostHog + print("Fetching training baseline from PostHog...") + df_raw = fetch_posthog_data(populated_query) + + # Isolate feature space (dropping IDs and raw timestamps) + feature_cols = [col for col in df_raw.columns if col not in ['person_id', 'session_id', 'session_start', 'session_end']] + df_features = df_raw[feature_cols].astype(float) + + # ------------------------------------------------------------------ + # Custom wrapper so inference handles scaling and returns scores + # ------------------------------------------------------------------ + class OneClassSVMWithScores(mlflow.pyfunc.PythonModel): + + def load_context(self, context): + import pickle + with open(context.artifacts["pipeline"], "rb") as f: + # This loads the combined StandardScaler + OneClassSVM pipeline + self.pipeline = pickle.load(f) + + def predict(self, context, model_input): + # Pipeline automatically scales inputs before passing them to the SVM + predictions = self.pipeline.predict(model_input) + raw_scores = self.pipeline.score_samples(model_input) + decision_scores = self.pipeline.decision_function(model_input) + + return pd.DataFrame( + { + "prediction": predictions, + "anomaly_score": raw_scores, + "decision_score": decision_scores, + } + ) + + with mlflow.start_run(run_name="PostHog_NuSVM_Baseline"): + + # ------------------------------------------------------------------ + # Dataset lineage tracking + # ------------------------------------------------------------------ + mlflow_dataset = mlflow.data.from_pandas( + df_features, + source=populated_query, + name="posthog_clean_sessions", + targets=None, + ) + + mlflow.log_input( + mlflow_dataset, + context="training", + tags={ + "sql_template_file": sql_filepath, + "start_date": start_date, + "end_date": end_date, + }, + ) + + # ------------------------------------------------------------------ + # Train Pipeline (Scaling + Nu-SVM / OneClassSVM) + # ------------------------------------------------------------------ + # For OneClassSVM, 'nu' acts as an upper bound on training errors + # and behaves similarly to the contamination rate. + nu_param = 0.01 + + mlflow.log_params({ + "nu": nu_param, + "kernel": "rbf", + "scaler": "StandardScaler" + }) + + print("Training Nu-SVM (One-Class SVM) Pipeline with feature scaling...") + + # Bundling preprocessing and model execution + svm_pipeline = Pipeline([ + ('scaler', StandardScaler()), + ('svm', OneClassSVM(nu=nu_param, kernel='rbf')) + ]) + + svm_pipeline.fit(df_features) + + # ------------------------------------------------------------------ + # Build example output signature + # ------------------------------------------------------------------ + example_output = pd.DataFrame( + { + "prediction": svm_pipeline.predict(df_features.head(5)), + "anomaly_score": svm_pipeline.score_samples(df_features.head(5)), + "decision_score": svm_pipeline.decision_function(df_features.head(5)), + } + ) + + signature = infer_signature( + df_features.head(5), + example_output, + ) + + # ------------------------------------------------------------------ + # Save pipeline artifact + # ------------------------------------------------------------------ + import pickle + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + pipeline_path = os.path.join(tmpdir, "svm_pipeline.pkl") + + with open(pipeline_path, "wb") as f: + pickle.dump(svm_pipeline, f) + + mlflow.pyfunc.log_model( + artifact_path="svm_anomaly_model", + python_model=OneClassSVMWithScores(), + artifacts={"pipeline": pipeline_path}, + signature=signature, + registered_model_name="NuSVM_Anomaly_Detector", + ) + + print("Model tracking and registry entry complete.") + +if __name__ == "__main__": + run_training_pipeline() \ No newline at end of file diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..d46b3e9 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2233 @@ +version = 1 +revision = 3 +requires-python = 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