def test_train_product_classifier_from_embeddings():
runner = CliRunner()
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
embeddings_path = common.get_full_path(tests_path,
common.TRANSFORMED_EMBEDDINGS_PATH)
# we are going to assume the working directory already has a embeddings model trained
params = "from_embeddings=True,label_col=categories,doc_col=document,classes=10,test_size=0.3," \
"lr=0.01,epochs=10,vec_size=300"
result = runner.invoke(cli.wtsp, [
'--work-dir', output_path, "train", "products", "--model",
"classifier", "--params", params, embeddings_path
])
assert result.exit_code == 0
# validate the existence of the output directory
result_dir = f"{output_path}/products/models/classifier"
assert os.path.exists(result_dir)
# and the content
assert os.path.exists(f"{result_dir}/category_encoder.model")
assert os.path.exists(f"{result_dir}/prod_classifier-def.yaml")
assert os.path.exists(f"{result_dir}/prod_classifier-weights.h5")
assert os.path.exists(f"{result_dir}/training_history.png")
assert os.path.exists(f"{result_dir}/classification_report.png")
common.delete_path(output_path)
def test_train_product_embeddings():
runner = CliRunner()
input_data = common.get_full_path(tests_path, common.RAW_PRODUCTS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
params = "label_col=categories,doc_col=document,lr=0.0002,epochs=10,vec_size=300,alpha=0.025,min_alpha=0.00025,min_count=1"
result = runner.invoke(cli.wtsp, [
'--work-dir', output_path, "train", "products", "--model",
"embeddings", "--params", params, input_data
])
assert result.exit_code == 0
# validate the existence of the output directory
result_dir = f"{output_path}/products/models/embeddings"
assert os.path.exists(result_dir)
# and the content
assert os.path.exists(f"{result_dir}/d2v_model.model")
common.delete_path(output_path)
def test_describe_tweets():
runner = CliRunner()
input_data = common.get_full_path(tests_path, common.RAW_TWEETS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
result = runner.invoke(cli.describe, [
'tweets', "--filters", "country_code=US", "--output-dir", output_path,
"--min-count", 10, input_data
])
assert result.exit_code == 0
# validate the existence of the output directory
tweets_describe_result = f"{output_path}/tweets/country_code=US"
assert os.path.exists(tweets_describe_result)
# and the content
assert os.path.exists(f"{tweets_describe_result}/counts.csv")
assert os.path.exists(f"{tweets_describe_result}/bar_chart.png")
common.delete_path(tweets_describe_result)
def test_describe_products_with_explode():
runner = CliRunner()
input_data = common.get_full_path(tests_path, common.RAW_PRODUCTS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
result = runner.invoke(cli.describe, [
'products', "--output-dir", output_path, "--groupby", "categories",
"--min-count", 10, "--explode", input_data
])
assert result.exit_code == 0
# validate the existence of the output directory
products_describe_result = f"{output_path}/documents"
assert os.path.exists(products_describe_result)
# and the content
assert os.path.exists(f"{products_describe_result}/counts.csv")
assert os.path.exists(f"{products_describe_result}/bar_chart.png")
common.delete_path(products_describe_result)
def test_train_tweets_n_neighbors():
runner = CliRunner()
input_data = common.get_full_path(tests_path, common.RAW_TWEETS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
result = runner.invoke(cli.train_tweets, [
'--model', "nearest-neighbors", "--filters",
"place_name='Los Angeles'", "--params",
"n_neighbors=10,location_column=location_geometry", "--output-dir",
output_path, input_data
])
assert result.exit_code == 0
# validate the existence of the output directory
result_dir = f"{output_path}/tweets/place_name=Los Angeles"
assert os.path.exists(result_dir)
# and the content
assert os.path.exists(f"{result_dir}/nearest_neighbors.png")
assert os.path.exists(f"{result_dir}/scatter_plot.png")
common.delete_path(output_path)
def test_transform_embeddings():
runner = CliRunner()
input_path = common.get_full_path(tests_path, common.RAW_PRODUCTS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
models_path = f"{output_path}/products/models/"
# we are going to assume the working directory already has a embeddings model trained
model_assets_path = common.get_full_path(tests_path, common.ASSETS_PATH)
copy_folder_recursively(f"{model_assets_path}/products", models_path)
result = runner.invoke(
cli.wtsp,
["--work-dir", output_path, "predict", "embeddings", input_path])
assert result.exit_code == 0
# validate the existence of the output files
result_embeddings = f"{output_path}/embeddings/document_embeddings.npz"
assert os.path.exists(result_embeddings)
result_cat_encoder = f"{output_path}/embeddings/category_encoder.save"
assert os.path.exists(result_cat_encoder)
common.delete_path(output_path)
def test_transform_where_to_sell_products():
runner = CliRunner()
input_data = common.get_full_path(tests_path, common.RAW_TWEETS_PATH)
output_path = common.get_full_path(tests_path, common.TEST_WORK_DIR_PATH)
models_path = f"{output_path}/products/models/"
# we are going to assume the working directory already has a embeddings model trained
model_assets_path = common.get_full_path(tests_path, common.ASSETS_PATH)
copy_folder_recursively(f"{model_assets_path}/products", models_path)
params = "center='34;-118',eps=0.04,n_neighbors=2,location_column=location_geometry,min_score=0.1"
result = runner.invoke(cli.wtsp, [
"--work-dir", output_path, "predict", "clusters", "--filters",
"place_name='Los Angeles'", "--params", params, input_data
])
assert result.exit_code == 0
# validate the existence of the output directory
result_dir = f"{output_path}/where_to_sell_in/place_name=Los Angeles"
assert os.path.exists(result_dir)
# and the content
assert os.path.exists(f"{result_dir}/classified_clusters.csv")
assert os.path.exists(f"{result_dir}/classified_clusters.html")
common.delete_path(output_path)