61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import pytest
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from delta_barth.analysis import forecast as fc
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def test_sales_per_customer_success(sales_data):
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customer_id = 1133
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res = fc.sales_per_customer(sales_data, customer_id)
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assert res.status.status_code == 0
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assert res.data is not None
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def test_sales_per_customer_too_few_data_points(sales_data):
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customer_id = 1000
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res = fc.sales_per_customer(sales_data, customer_id)
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assert res.status.status_code == 1
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assert res.data is None
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def test_parse_api_resp_to_df(exmpl_api_sales_prognosis_resp):
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resp = exmpl_api_sales_prognosis_resp
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df = fc.parse_api_resp_to_df(resp)
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features = set(
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(
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"artikelId",
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"warengruppeId",
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"firmaId",
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"betrag",
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"menge",
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"buchungsDatum",
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)
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)
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assert all(col in features for col in df.columns)
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def test_preprocess_sales_per_customer(exmpl_api_sales_prognosis_resp):
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resp = exmpl_api_sales_prognosis_resp
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feat_mapping: dict[str, str] = {
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"artikelId": "artikel_refid",
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"firmaId": "firma_refid",
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"betrag": "betrag",
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"menge": "menge",
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"buchungsDatum": "buchungs_datum",
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}
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target_features: frozenset[str] = frozenset(
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(
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"firma_refid",
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"betrag",
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"buchungs_datum",
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)
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)
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df = fc.preprocess_sales_per_customer(
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resp,
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feature_map=feat_mapping,
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target_features=target_features,
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)
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assert len(df.columns) == 5
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assert any(feat not in df.columns for feat in feat_mapping.keys())
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