60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
import pandas as pd
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import pytest
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from delta_barth.analysis import forecast, parse
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from delta_barth.errors import FeaturesMissingError
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def test_check_needed_features():
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target_features = set(("feat1", "feat2", "feat3"))
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data = pd.DataFrame(
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data=[[1, 2, 3, 4, 5]], columns=["feat1", "feat2", "feat3", "feat4", "feat5"]
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)
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parse._check_needed_features(data, target_features)
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data = pd.DataFrame(
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data=[[1, 2, 3, 4, 5]], columns=["featX", "feat2", "feat3", "feat4", "feat5"]
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)
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with pytest.raises(FeaturesMissingError):
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parse._check_needed_features(data, target_features)
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def test_map_features_to_targets():
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feature_map = dict(feat1="feat10", feat2="feat20", feat5="feat50")
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data = pd.DataFrame(
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data=[[1, 2, 3, 4, 5]], columns=["feat1", "feat2", "feat3", "feat4", "feat5"]
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)
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data = parse._map_features_to_targets(data, feature_map)
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assert "feat10" in data.columns
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assert "feat20" in data.columns
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assert "feat50" in data.columns
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assert "feat3" in data.columns
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assert "feat4" in data.columns
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assert "feat1" not in data.columns
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assert "feat2" not in data.columns
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assert "feat5" not in data.columns
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def test_preprocess_features(exmpl_api_sales_prognosis_resp):
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resp = exmpl_api_sales_prognosis_resp
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df = forecast.parse_api_resp_to_df(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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assert all(feat in df.columns for feat in feat_mapping.keys())
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data = parse.preprocess_features(df, feat_mapping, target_features)
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assert len(data.columns) == len(df.columns)
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assert (data.columns != df.columns).any()
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assert any(feat not in data.columns for feat in feat_mapping.keys())
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