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Add unit test for lightweight SVM sentiment analysis #2

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6 changes: 6 additions & 0 deletions .ipynb_checkpoints/Sentiment_Analysis_SVM_Scikit-checkpoint.ipynb
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6 changes: 6 additions & 0 deletions .ipynb_checkpoints/tests_sentiment_analysis_svm_scikit-checkpoint.ipynb
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747 changes: 747 additions & 0 deletions Sentiment_Analysis_SVM_Scikit.ipynb
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184 changes: 184 additions & 0 deletions logs.log
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2025-10-10 22:54:20,400:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2025-10-10 22:54:20,400:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2025-10-10 22:54:20,400:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2025-10-10 22:54:20,400:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2025-10-10 22:54:41,537:INFO:PyCaret ClassificationExperiment
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2025-10-10 22:54:41,538:INFO:version 3.3.2
2025-10-10 22:54:41,538:INFO:Initializing setup()
2025-10-10 22:54:41,538:INFO:self.USI: b3fb
2025-10-10 22:54:41,538:INFO:self._variable_keys: {'seed', 'y_train', 'X_train', 'y', 'X_test', 'exp_id', 'memory', 'target_param', 'fix_imbalance', 'gpu_param', 'X', 'is_multiclass', 'gpu_n_jobs_param', 'idx', 'pipeline', 'n_jobs_param', 'y_test', 'logging_param', '_available_plots', '_ml_usecase', 'fold_shuffle_param', 'log_plots_param', 'fold_groups_param', 'exp_name_log', 'fold_generator', 'html_param', 'data', 'USI'}
2025-10-10 22:54:41,538:INFO:Checking environment
2025-10-10 22:54:41,538:INFO:python_version: 3.10.18
2025-10-10 22:54:41,538:INFO:python_build: ('main', 'Jun 5 2025 08:13:51')
2025-10-10 22:54:41,538:INFO:machine: x86_64
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2025-10-10 22:54:41,540:INFO:Physical Core: 4
2025-10-10 22:54:41,540:INFO:Logical Core: 8
2025-10-10 22:54:41,540:INFO:Checking libraries
2025-10-10 22:54:41,540:INFO:System:
2025-10-10 22:54:41,540:INFO: python: 3.10.18 (main, Jun 5 2025, 08:13:51) [Clang 14.0.6 ]
2025-10-10 22:54:41,540:INFO:executable: /opt/miniconda3/envs/py310/bin/python
2025-10-10 22:54:41,540:INFO: machine: macOS-10.16-x86_64-i386-64bit
2025-10-10 22:54:41,540:INFO:PyCaret required dependencies:
2025-10-10 22:54:44,930:INFO: pip: 25.2
2025-10-10 22:54:44,930:INFO: setuptools: 78.1.1
2025-10-10 22:54:44,930:INFO: pycaret: 3.3.2
2025-10-10 22:54:44,930:INFO: IPython: 8.30.0
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2025-10-10 22:54:44,931:INFO: pandas: 2.1.4
2025-10-10 22:54:44,931:INFO: jinja2: 3.1.6
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2025-10-10 22:54:44,931:INFO: joblib: 1.3.2
2025-10-10 22:54:44,931:INFO: sklearn: 1.4.2
2025-10-10 22:54:44,931:INFO: pyod: 2.0.5
2025-10-10 22:54:44,931:INFO: imblearn: 0.14.0
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2025-10-10 22:54:44,931:INFO: lightgbm: 4.6.0
2025-10-10 22:54:44,931:INFO: numba: 0.61.0
2025-10-10 22:54:44,931:INFO: requests: 2.32.4
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2025-10-10 22:54:44,932:INFO: deprecation: 2.1.0
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2025-10-10 22:55:07,338:INFO: deepchecks: Not installed
2025-10-10 22:55:07,338:INFO: xgboost: 3.0.5
2025-10-10 22:55:07,338:INFO: catboost: 1.1.1
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2025-10-10 22:55:07,339:INFO: prophet: Not installed
2025-10-10 22:55:07,339:INFO:None
2025-10-10 22:55:07,339:INFO:Set up data.
2025-10-10 22:55:07,365:INFO:Set up folding strategy.
2025-10-10 22:55:07,365:INFO:Set up train/test split.
2025-10-10 22:55:07,380:INFO:Set up index.
2025-10-10 22:55:07,380:INFO:Assigning column types.
2025-10-10 22:55:07,384:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2025-10-10 22:55:07,443:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2025-10-10 22:55:07,448:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2025-10-10 22:55:07,495:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:07,499:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:08,587:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2025-10-10 22:55:08,588:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2025-10-10 22:55:08,625:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:08,628:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:08,629:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2025-10-10 22:55:08,688:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2025-10-10 22:55:08,724:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:08,728:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:08,788:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2025-10-10 22:55:08,825:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:08,828:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:08,829:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2025-10-10 22:55:08,927:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:08,930:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:09,026:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:09,030:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:09,035:INFO:Preparing preprocessing pipeline...
2025-10-10 22:55:09,037:INFO:Set up simple imputation.
2025-10-10 22:55:09,037:INFO:Set up text embedding.
2025-10-10 22:55:09,496:INFO:Finished creating preprocessing pipeline.
2025-10-10 22:55:09,501:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/yj/3b60s0r14sd5g_658xzzpr1r0000gn/T/joblib),
steps=[('numerical_imputer',
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method='tf-idf')))],
verbose=False)
2025-10-10 22:55:09,502:INFO:Creating final display dataframe.
2025-10-10 22:55:21,714:INFO:Setup _display_container: Description Value
0 Session id 42
1 Target label
2 Target type Multiclass
3 Original data shape (9000, 2)
4 Transformed data shape (9000, 14266)
5 Transformed train set shape (6300, 14266)
6 Transformed test set shape (2700, 14266)
7 Text features 1
8 Preprocess True
9 Imputation type simple
10 Numeric imputation mean
11 Categorical imputation mode
12 Text features embedding method tf-idf
13 Fold Generator StratifiedKFold
14 Fold Number 3
15 CPU Jobs -1
16 Use GPU False
17 Log Experiment False
18 Experiment Name clf-default-name
19 USI b3fb
2025-10-10 22:55:21,814:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:21,818:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:21,915:INFO:Soft dependency imported: xgboost: 3.0.5
2025-10-10 22:55:21,918:INFO:Soft dependency imported: catboost: 1.1.1
2025-10-10 22:55:21,921:INFO:setup() successfully completed in 40.38s...............
2025-10-10 22:55:40,926:INFO:Initializing create_model()
2025-10-10 22:55:40,927:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x108503f40>, estimator=svm, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2025-10-10 22:55:40,927:INFO:Checking exceptions
2025-10-10 22:55:40,958:INFO:Importing libraries
2025-10-10 22:55:40,958:INFO:Copying training dataset
2025-10-10 22:55:40,969:INFO:Defining folds
2025-10-10 22:55:40,970:INFO:Declaring metric variables
2025-10-10 22:55:40,977:INFO:Importing untrained model
2025-10-10 22:55:40,983:INFO:SVM - Linear Kernel Imported successfully
2025-10-10 22:55:40,994:INFO:Starting cross validation
2025-10-10 22:55:41,157:INFO:Cross validating with StratifiedKFold(n_splits=3, random_state=None, shuffle=False), n_jobs=-1
Binary file added tests/__pycache__/test_sentiment_analysis_svm_scikit.cpython-310-pytest-7.4.4.pyc
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24 changes: 24 additions & 0 deletions tests/test_sentiment_analysis_svm_scikit.ipynb
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"""
Unit test for the SVM Sentiment Analysis example.
Ensures the model can train and predict without errors.
"""

import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

def tests_model_prediction():
"""Train an SVM and check predictions."""
df = pd.DataFrame({
"text": ["I love this!", "This is bad.", "It's okay."],
"label": [2, 0, 1] # 2=Positive, 0=Negative, 1=Neutral
})

model = make_pipeline(TfidfVectorizer(), LinearSVC())
model.fit(df["text"], df["label"])

preds = model.predict(["Awesome product!", "Horrible experience"])
assert all(p in [0, 1, 2] for p in preds)

print("Unit test passed successfully.")
24 changes: 24 additions & 0 deletions tests/test_sentiment_analysis_svm_scikit.py
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"""
Unit test for the SVM Sentiment Analysis example.
Ensures the model can train and predict without errors.
"""

import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

def tests_model_prediction():
"""Train an SVM and check predictions."""
df = pd.DataFrame({
"text": ["I love this!", "This is bad.", "It's okay."],
"label": [2, 0, 1] # 2=Positive, 0=Negative, 1=Neutral
})

model = make_pipeline(TfidfVectorizer(), LinearSVC())
model.fit(df["text"], df["label"])

preds = model.predict(["Awesome product!", "Horrible experience"])
assert all(p in [0, 1, 2] for p in preds)

print("Unit test passed successfully.")
66 changes: 66 additions & 0 deletions tests_sentiment_analysis_svm_scikit.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "55acd39d-3a31-44c7-8cab-ffd0ec339bc9",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Unit test for the SVM Sentiment Analysis example.\n",
"Ensures the model can train and predict without errors.\n",
"\"\"\"\n",
"\n",
"import pandas as pd\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.svm import LinearSVC\n",
"\n",
"def tests_model_prediction():\n",
" \"\"\"Train an SVM and check predictions.\"\"\"\n",
" df = pd.DataFrame({\n",
" \"text\": [\"I love this!\", \"This is bad.\", \"It's okay.\"],\n",
" \"label\": [2, 0, 1] # 2=Positive, 0=Negative, 1=Neutral\n",
" })\n",
"\n",
" model = make_pipeline(TfidfVectorizer(), LinearSVC())\n",
" model.fit(df[\"text\"], df[\"label\"])\n",
"\n",
" preds = model.predict([\"Awesome product!\", \"Horrible experience\"])\n",
" assert all(p in [0, 1, 2] for p in preds)\n",
"\n",
" print(\"Unit test passed successfully.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac1fec51-4881-4997-8f32-f6f0e47c3daf",
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"source": []
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