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993fce6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | # User Guide
## Overview
`RiskEnsembleClassifier.pkl` is a trained machine learning model that classifies policy clauses into four risk levels: **Critical**, **High**, **Medium**, and **Low**. It bundles the ensemble model along with all the fitted transformers needed to reproduce predictions.
---
## Step 1 — Generate the Pickle File
Run `RiskEnsembleClassifier.py` from inside the `Machine_Learning` folder. This trains the model on `output/master_dataset.csv` and saves the pickle file in the same directory.
> **Prerequisite:** Install dependencies first.
```bash
# From the Machine_Learning directory
pip install -r requirements.txt
python RiskEnsembleClassifier.py
```
On success you will see:
```
[RiskEnsembleClassifier] Model saved to RiskEnsembleClassifier.pkl
Accuracy : 91.5%
Critical Recall: 0.90xx
Base models : XGBoost + LightGBM + ExtraTrees
```
---
## Step 2 — What's Inside the Pickle
The pickle file is a Python `dict` with these keys:
| Key | Description |
|---|---|
| `model` | Trained `VotingClassifier` (XGBoost + LightGBM + ExtraTrees) |
| `tfidf_word` | Fitted word n-gram `TfidfVectorizer` (max 2 000 features) |
| `tfidf_char` | Fitted char n-gram `TfidfVectorizer` (max 1 000 features) |
| `scaler` | Fitted `StandardScaler` for numeric columns |
| `label_encoder` | Fitted `LabelEncoder` → maps integers back to risk labels |
| `num_cols` | List of numeric feature column names |
---
## Step 3 — Load and Use the Model
```python
import pickle
import pandas as pd
import scipy.sparse as sp
# ── 1. Load ────────────────────────────────────────────────────────────────
with open("RiskEnsembleClassifier.pkl", "rb") as f:
bundle = pickle.load(f)
model = bundle["model"]
tfidf_w = bundle["tfidf_word"]
tfidf_c = bundle["tfidf_char"]
scaler = bundle["scaler"]
le = bundle["label_encoder"]
num_cols = bundle["num_cols"]
# ── 2. Prepare your data ───────────────────────────────────────────────────
# Each clause must have:
# clean_text – pre-processed clause text (lowercase, stop-words removed)
# modal_score – float [0, 1]
# consequence_score – float [0, 1]
# conditional_score – float [0, 1]
# has_negation – int 0 or 1
# obligation_count – int ≥ 0
# penalty_flag – int 0 or 1
# word_count – int ≥ 1
clauses = [
{
"clean_text": "employee must never share trade secret proprietary data third party",
"modal_score": 0.98, "consequence_score": 0.99, "conditional_score": 0.90,
"has_negation": 1, "obligation_count": 4, "penalty_flag": 1, "word_count": 12
},
{
"clean_text": "employee may work from home friday subject manager approval",
"modal_score": 0.12, "consequence_score": 0.08, "conditional_score": 0.15,
"has_negation": 0, "obligation_count": 0, "penalty_flag": 0, "word_count": 10
},
]
df = pd.DataFrame(clauses)
# ── 3. Transform features ──────────────────────────────────────────────────
word = tfidf_w.transform(df["clean_text"])
char = tfidf_c.transform(df["clean_text"])
num = sp.csr_matrix(scaler.transform(df[num_cols]))
X = sp.hstack([word, char, num], format="csr")
# ── 4. Predict ─────────────────────────────────────────────────────────────
predictions = le.inverse_transform(model.predict(X))
for i, (pred, clause) in enumerate(zip(predictions, clauses)):
print(f"Clause {i+1}: [{pred}] → {clause['clean_text'][:60]}...")
```
**Expected output:**
```
Clause 1: [Critical] → employee must never share trade secret proprietary data...
Clause 2: [Low] → employee may work from home friday subject manager appro...
```
---
## Risk Label Reference
| Label | Meaning |
|---|---|
| `Critical` | Severe obligation / strong penalty — requires immediate legal review |
| `High` | Significant obligation — needs close attention |
| `Medium` | Moderate obligation — routine monitoring |
| `Low` | Permissive / informational — low priority |
---
## Notes
- **sklearn version** — The pickle was generated with `scikit-learn 1.8.0`. Loading with an older version may show `InconsistentVersionWarning`; predictions still work but it is recommended to match versions.
- **clean_text format** — Feed pre-processed text (lowercased, punctuation stripped). The model was trained on such text.
- For a broader test with 20 sample clauses across all four risk levels, see `notebooks/model_testing.ipynb`.
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